qing
a18a7de1ca
分箱（bins.py）：舵机 1°/箱 ×10（180 箱，匹配 0.75° 微动作插值粒度）、 spk.F/spk.delay 对数 32 箱 ×2，共 1864 logits；保留位无头。软标签 （onehot_sigma=1.0 高斯扩散）缓解小样本数据稀疏；期望值解码恢复 连续分辨率（箱生活在原始空间，无反变换放大）。 训练损失为逐头加权 CE（列权重沿用），闭环反馈经解码→范围归一化 回写历史；predict 输出仍为 18 维原始量纲，pybot 契约零变化。 结构性消除尺度放大（每箱等距）与物理界问题（箱天然有界）， 为 A-E 归一化实验失败后的正解路线。要求 normalize_mode 为范围类， 与 predict_delta 互斥。测试 +10（含 CLI 映射回归）。
358 lines
18 KiB
Python
358 lines
18 KiB
Python
import argparse
|
||
from dataclasses import dataclass, asdict, field
|
||
from typing import List, Optional
|
||
|
||
|
||
@dataclass
|
||
class DataConfig:
|
||
image_width: int = 640
|
||
image_height: int = 480
|
||
action_dim: int = 18
|
||
history_len: int = 5
|
||
frame_len: int = 3
|
||
data_dir: str = "./data"
|
||
normalize_actions: bool = True
|
||
action_stats_path: Optional[str] = None
|
||
# 归一化方式（仅 normalize_actions=True 时生效）：
|
||
# "zscore" 数据统计 z-score（现状，依赖 action_stats.npy）
|
||
# "fixed_range" 固定物理范围 min-max → [0,1]（免 stats 文件，零状态部署，
|
||
# 常量表见 action_norm.py；越界值 clamp 防御）
|
||
# "log_range" 同上，但 spk.F/spk.delay 两列用 log10(1+x) 对数尺度，
|
||
# 修正低端聚集导致的精度错配（见实验报告）
|
||
normalize_mode: str = "zscore"
|
||
# 逐列损失权重（仅训练损失用，推理端无感知）：None → action_norm
|
||
# .default_column_weights(action_dim)（舵机 1.0 / spk 0.2 / 保留位 0.0）。
|
||
# 显式指定时长度须等于 action_dim。
|
||
column_weights: Optional[List[float]] = None
|
||
# 多头 one-hot 分类输出（bins.py 分箱）：True 时监督目标为多组 one-hot
|
||
# （舵机 1°/箱 ×10、spk 对数 32 箱 ×2，共 1864 logits），损失用加权 CE，
|
||
# 推理端期望值解码。要求 normalize_actions=True 且 normalize_mode 为
|
||
# 范围类（fixed_range/log_range）——历史输入沿用范围归一化，免 stats。
|
||
# 与 predict_delta 互斥（残差在连续空间定义）。
|
||
onehot: bool = False
|
||
# one-hot 软标签高斯宽度（箱）：0=硬标签；>0 真值扩散到邻近箱，
|
||
# 缓解小样本下分类面数据稀疏（1°/箱 × 804 样本多数箱极少被访问）
|
||
onehot_sigma: float = 1.0
|
||
# R2 方案一：MPU6050 姿态作为独立 IMU token 注入（序列 F+H+2）。
|
||
# 0 = 关闭（默认，序列保持 F+H+1，旧 checkpoint 完全兼容）；
|
||
# 8×N = N 个传感器的 roll,pitch,ax,ay,az,gx,gy,gz 按 pybot 脚本
|
||
# @MPU6050_BIND 行顺序拼接（最多 4 个 → 32，pybot recorder 的 imu.npy 格式）。
|
||
imu_dim: int = 0
|
||
|
||
|
||
@dataclass
|
||
class ModelConfig:
|
||
cnn_backbone: str = "simple"
|
||
cnn_feature_dim: int = 128
|
||
fused_hidden_dim: int = 128
|
||
dropout: float = 0.1
|
||
attn_dim: int = 128
|
||
attn_heads: int = 4
|
||
attn_layers: int = 2
|
||
|
||
|
||
@dataclass
|
||
class TrainConfig:
|
||
# 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
|
||
# 端到端延迟，见测速报告 §6.1）；更高分辨率训练时需调小 batch
|
||
batch_size: int = 4
|
||
epochs: int = 100
|
||
lr: float = 1e-3
|
||
weight_decay: float = 1e-4
|
||
val_split: float = 0.15
|
||
checkpoint_dir: str = "./checkpoints"
|
||
patience: int = 15
|
||
num_workers: int = 2
|
||
seed: int = 42
|
||
max_grad_norm: float = 1.0
|
||
augment: bool = False
|
||
# R1-C 残差预测：True 时 target 改为 Δ=actions[t]-actions[t-1]，
|
||
# 推理端 â_t = a_{t-1} + Δ̂（惯性先验，平滑动作）。默认 False 兼容旧 checkpoint。
|
||
predict_delta: bool = False
|
||
# R1-B 多步展开训练：rollout_len>1 时 dataset 返回 K 步连续序列，
|
||
# 训练用 scheduled sampling（前 ss_prob 比例步用真历史，其余用自预测回传），
|
||
# 治 exposure bias。rollout_len=1 时退化为现状单步。
|
||
rollout_len: int = 1
|
||
ss_prob: float = 1.0
|
||
|
||
|
||
@dataclass
|
||
class Config:
|
||
data: DataConfig = field(default_factory=DataConfig)
|
||
model: ModelConfig = field(default_factory=ModelConfig)
|
||
train: TrainConfig = field(default_factory=TrainConfig)
|
||
|
||
def to_dict(self):
|
||
return asdict(self)
|
||
|
||
@classmethod
|
||
def from_dict(cls, d):
|
||
return cls(
|
||
data=DataConfig(**d.get("data", {})),
|
||
model=ModelConfig(**d.get("model", {})),
|
||
train=TrainConfig(**d.get("train", {})),
|
||
)
|
||
|
||
|
||
def build_argparser(description: str = "") -> argparse.ArgumentParser:
|
||
parser = argparse.ArgumentParser(description=description)
|
||
parser.add_argument("--image-width", type=int, default=None)
|
||
parser.add_argument("--image-height", type=int, default=None)
|
||
parser.add_argument("--action-dim", type=int, default=None)
|
||
parser.add_argument("--history-len", type=int, default=None)
|
||
parser.add_argument("--frame-len", type=int, default=None)
|
||
parser.add_argument("--data-dir", type=str, default=None)
|
||
parser.add_argument("--normalize-actions", action=argparse.BooleanOptionalAction, default=None)
|
||
parser.add_argument("--action-stats-path", type=str, default=None)
|
||
parser.add_argument("--normalize-mode", type=str, default=None,
|
||
choices=["zscore", "fixed_range", "log_range"],
|
||
help="动作归一化方式：zscore=数据统计（依赖 action_stats.npy）；"
|
||
"fixed_range=固定物理范围 min-max→[0,1]（免 stats 文件）；"
|
||
"log_range=spk 两列对数尺度（免 stats 文件）")
|
||
parser.add_argument("--onehot", action=argparse.BooleanOptionalAction,
|
||
default=None,
|
||
help="多头 one-hot 分类输出（bins.py 分箱，舵机 1°/箱）")
|
||
parser.add_argument("--onehot-sigma", type=float, default=None,
|
||
help="one-hot 软标签高斯宽度（箱，0=硬标签，默认 1.0）")
|
||
parser.add_argument("--column-weights", type=str, default=None,
|
||
help="逐列损失权重（逗号分隔 action_dim 个浮点），"
|
||
"缺省用默认表（舵机1.0/spk0.2/保留位0.0）；仅影响训练损失")
|
||
parser.add_argument("--imu-dim", type=int, default=None,
|
||
help="R2 方案一：MPU6050 姿态维度（0=关闭，默认；"
|
||
"8×N=N 个传感器拼接（≤4 个），序列变 F+H+2）")
|
||
parser.add_argument("--backbone", type=str, default=None, choices=["simple", "mobilenet"])
|
||
parser.add_argument("--cnn-feature-dim", type=int, default=None)
|
||
parser.add_argument("--fused-hidden-dim", type=int, default=None)
|
||
parser.add_argument("--dropout", type=float, default=None)
|
||
parser.add_argument("--attn-dim", type=int, default=None)
|
||
parser.add_argument("--attn-heads", type=int, default=None)
|
||
parser.add_argument("--attn-layers", type=int, default=None)
|
||
parser.add_argument("--batch-size", type=int, default=None)
|
||
parser.add_argument("--epochs", type=int, default=None)
|
||
parser.add_argument("--lr", type=float, default=None)
|
||
parser.add_argument("--weight-decay", type=float, default=None)
|
||
parser.add_argument("--val-split", type=float, default=None)
|
||
parser.add_argument("--checkpoint-dir", type=str, default=None)
|
||
parser.add_argument("--patience", type=int, default=None)
|
||
parser.add_argument("--num-workers", type=int, default=None)
|
||
parser.add_argument("--seed", type=int, default=None)
|
||
parser.add_argument("--max-grad-norm", type=float, default=None)
|
||
parser.add_argument("--augment", action=argparse.BooleanOptionalAction, default=None,
|
||
help="训练时启用安全图像增强（ColorJitter：亮度/对比度/饱和度/色调）")
|
||
parser.add_argument("--predict-delta", action=argparse.BooleanOptionalAction, default=None,
|
||
help="R1-C 残差预测：target 改为 Δ=actions[t]-actions[t-1]，"
|
||
"推理端 â_t=a_{t-1}+Δ̂（惯性先验平滑动作，默认关兼容旧 checkpoint）")
|
||
parser.add_argument("--rollout-len", type=int, default=None,
|
||
help="R1-B 多步展开训练步数 K（默认 1=单步；K>1 时 scheduled sampling）")
|
||
parser.add_argument("--ss-prob", type=float, default=None,
|
||
help="scheduled sampling 真实历史概率（仅 rollout-len>1 生效，默认 1.0=全真）")
|
||
parser.add_argument("--init-from", type=str, default=None,
|
||
help="预训练 checkpoint 路径，作为微调起点（strict=False 加载兼容权重）")
|
||
parser.add_argument("--resume", type=str, default=None,
|
||
help="从 last.pt 断点续训（恢复 model/optimizer/scheduler/epoch）")
|
||
parser.add_argument("--save-config", type=str, default=None, help="保存配置到 JSON 文件")
|
||
return parser
|
||
|
||
|
||
def validate_config_values(data_cfg: DataConfig, model_cfg: ModelConfig,
|
||
train_cfg: TrainConfig = None):
|
||
"""校验从 checkpoint 恢复或 CLI 传入的配置值域合理性
|
||
|
||
防止恶意/损坏 checkpoint 注入异常值（如超大维度导致 OOM），
|
||
也防止训练 CLI 越界参数（如 --history-len 0 / --lr -1）静默进入训练。
|
||
"""
|
||
checks = [
|
||
("image_width", data_cfg.image_width, 1, 4096, int),
|
||
("image_height", data_cfg.image_height, 1, 4096, int),
|
||
("history_len", data_cfg.history_len, 1, 1024, int),
|
||
("frame_len", data_cfg.frame_len, 1, 1024, int),
|
||
("action_dim", data_cfg.action_dim, 1, 1024, int),
|
||
("imu_dim", data_cfg.imu_dim, 0, 64, int),
|
||
("cnn_feature_dim", model_cfg.cnn_feature_dim, 1, 4096, int),
|
||
("fused_hidden_dim", model_cfg.fused_hidden_dim, 1, 4096, int),
|
||
("dropout", model_cfg.dropout, 0.0, 1.0, float),
|
||
("attn_dim", model_cfg.attn_dim, 1, 4096, int),
|
||
("attn_heads", model_cfg.attn_heads, 1, 64, int),
|
||
("attn_layers", model_cfg.attn_layers, 1, 64, int),
|
||
]
|
||
for name, val, lo, hi, typ in checks:
|
||
if typ is int:
|
||
ok = isinstance(val, int) and not isinstance(val, bool) and lo <= val <= hi
|
||
else:
|
||
ok = isinstance(val, (int, float)) and not isinstance(val, bool) and lo <= val <= hi
|
||
if not ok:
|
||
raise ValueError(
|
||
f"checkpoint config {name}={val} 超出合理范围 [{lo}, {hi}]")
|
||
|
||
if model_cfg.attn_dim % model_cfg.attn_heads != 0:
|
||
raise ValueError(
|
||
f"attn_dim={model_cfg.attn_dim} 必须能被 attn_heads="
|
||
f"{model_cfg.attn_heads} 整除")
|
||
|
||
if not (data_cfg.normalize_mode in ("zscore", "fixed_range", "log_range")):
|
||
raise ValueError(
|
||
f"normalize_mode={data_cfg.normalize_mode!r} 非法，"
|
||
f"应为 'zscore'、'fixed_range' 或 'log_range'")
|
||
|
||
if data_cfg.onehot:
|
||
if not data_cfg.normalize_actions:
|
||
raise ValueError("onehot=True 要求 normalize_actions=True"
|
||
"（历史输入须范围归一化）")
|
||
if not (data_cfg.normalize_mode in ("fixed_range", "log_range")):
|
||
raise ValueError(
|
||
f"onehot=True 要求 normalize_mode 为范围类，"
|
||
f"实际为 {data_cfg.normalize_mode!r}")
|
||
if train_cfg.predict_delta:
|
||
raise ValueError("onehot 与 predict_delta 互斥"
|
||
"（残差定义在连续空间）")
|
||
if not (0.0 <= data_cfg.onehot_sigma <= 10.0):
|
||
raise ValueError(
|
||
f"onehot_sigma={data_cfg.onehot_sigma} 应在 [0, 10] 内")
|
||
|
||
if data_cfg.column_weights is not None:
|
||
w = data_cfg.column_weights
|
||
if len(w) != data_cfg.action_dim:
|
||
raise ValueError(
|
||
f"column_weights 长度 {len(w)} 应等于 action_dim="
|
||
f"{data_cfg.action_dim}")
|
||
if any((not isinstance(v, (int, float))) or isinstance(v, bool)
|
||
or not (0.0 <= v <= 1e6) for v in w):
|
||
raise ValueError(
|
||
f"column_weights 元素应为 [0, 1e6] 内的数值，实际: {w}")
|
||
if not any(v > 0 for v in w):
|
||
raise ValueError("column_weights 全 0：损失恒 0，训练无梯度")
|
||
|
||
if train_cfg is not None:
|
||
t_checks = [
|
||
("batch_size", train_cfg.batch_size, 1, 65536, int),
|
||
("epochs", train_cfg.epochs, 1, 100000, int),
|
||
("lr", train_cfg.lr, 1e-9, 10.0, float),
|
||
("weight_decay", train_cfg.weight_decay, 0.0, 1.0, float),
|
||
("val_split", train_cfg.val_split, 0.0, 1.0, float),
|
||
("patience", train_cfg.patience, 0, 100000, int),
|
||
("num_workers", train_cfg.num_workers, 0, 256, int),
|
||
("max_grad_norm", train_cfg.max_grad_norm, 0.0, 1e6, float),
|
||
("rollout_len", train_cfg.rollout_len, 1, 1024, int),
|
||
("ss_prob", train_cfg.ss_prob, 0.0, 1.0, float),
|
||
]
|
||
for name, val, lo, hi, typ in t_checks:
|
||
if typ is int:
|
||
ok = isinstance(val, int) and not isinstance(val, bool) and lo <= val <= hi
|
||
else:
|
||
ok = isinstance(val, (int, float)) and not isinstance(val, bool) and lo <= val <= hi
|
||
if not ok:
|
||
raise ValueError(
|
||
f"配置 {name}={val} 超出合理范围 [{lo}, {hi}]")
|
||
|
||
|
||
def warn_init_from_mismatch(init_cfg: dict, current: Config):
|
||
"""--init-from 加载预训练权重前的兼容性警告（train.py / pretrain_mt.py 共用）
|
||
|
||
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；
|
||
action_dim 不一致时 act_proj 无法加载对应权重（strict=False 静默跳过，
|
||
该层随机初始化）。
|
||
"""
|
||
if not isinstance(init_cfg, dict):
|
||
return
|
||
init_data = init_cfg.get("data") or {}
|
||
init_norm = init_data.get("normalize_actions", False)
|
||
if init_norm != current.data.normalize_actions:
|
||
print(f"WARNING: init-from checkpoint 的 normalize_actions="
|
||
f"{init_norm} 与当前运行 {current.data.normalize_actions} "
|
||
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）")
|
||
init_mode = init_data.get("normalize_mode", "zscore") # 旧 checkpoint 无此键 → zscore
|
||
if init_norm and current.data.normalize_actions and init_mode != current.data.normalize_mode:
|
||
print(f"WARNING: init-from checkpoint 的 normalize_mode={init_mode} "
|
||
f"与当前运行 {current.data.normalize_mode} 不一致，"
|
||
f"归一化空间不同会损失预训练收益")
|
||
init_dim = init_data.get("action_dim")
|
||
if init_dim is not None and init_dim != current.data.action_dim:
|
||
print(f"WARNING: init-from checkpoint 的 action_dim={init_dim} "
|
||
f"与当前运行 {current.data.action_dim} 不一致，"
|
||
f"act_proj 权重将随机初始化")
|
||
init_imu = init_data.get("imu_dim", 0) # 旧 checkpoint 无此键 → 0
|
||
if init_imu != current.data.imu_dim:
|
||
print(f"WARNING: init-from checkpoint 的 imu_dim={init_imu} "
|
||
f"与当前运行 {current.data.imu_dim} 不一致，"
|
||
f"imu_proj/pos_embed 权重将随机初始化")
|
||
|
||
|
||
def parse_checkpoint_config(cfg_dict: dict) -> Config:
|
||
"""从 checkpoint 恢复 Config，校验键集合与值域
|
||
|
||
- 顶层缺 data/model 键 → ValueError（带明确信息）
|
||
- data/model/train 段含未知键 → ValueError（列出未知键，避免裸 TypeError）
|
||
- 值域非法 → ValueError（复用 validate_config_values）
|
||
"""
|
||
if not isinstance(cfg_dict, dict):
|
||
raise ValueError(f"checkpoint config 应为 dict，实际为 {type(cfg_dict).__name__}")
|
||
section_dataclasses = {
|
||
"data": DataConfig,
|
||
"model": ModelConfig,
|
||
"train": TrainConfig,
|
||
}
|
||
for section, dataclass in section_dataclasses.items():
|
||
if section not in cfg_dict:
|
||
if section in ("data", "model"):
|
||
raise ValueError(f"checkpoint config 缺少 '{section}' 段")
|
||
continue
|
||
section_dict = cfg_dict[section]
|
||
if not isinstance(section_dict, dict):
|
||
raise ValueError(
|
||
f"checkpoint config['{section}'] 应为 dict，"
|
||
f"实际为 {type(section_dict).__name__}")
|
||
unknown = set(section_dict) - set(dataclass.__dataclass_fields__)
|
||
if unknown:
|
||
raise ValueError(
|
||
f"checkpoint config['{section}'] 含未知键: {sorted(unknown)}")
|
||
cfg = Config.from_dict(cfg_dict)
|
||
validate_config_values(cfg.data, cfg.model, cfg.train)
|
||
return cfg
|
||
|
||
|
||
def apply_overrides(cfg: Config, args: argparse.Namespace) -> Config:
|
||
mapping = {
|
||
"image_width": ("data", "image_width"),
|
||
"image_height": ("data", "image_height"),
|
||
"action_dim": ("data", "action_dim"),
|
||
"history_len": ("data", "history_len"),
|
||
"frame_len": ("data", "frame_len"),
|
||
"data_dir": ("data", "data_dir"),
|
||
"normalize_actions": ("data", "normalize_actions"),
|
||
"action_stats_path": ("data", "action_stats_path"),
|
||
"normalize_mode": ("data", "normalize_mode"),
|
||
"column_weights": ("data", "column_weights"),
|
||
"onehot": ("data", "onehot"),
|
||
"onehot_sigma": ("data", "onehot_sigma"),
|
||
"imu_dim": ("data", "imu_dim"),
|
||
"backbone": ("model", "cnn_backbone"),
|
||
"cnn_feature_dim": ("model", "cnn_feature_dim"),
|
||
"fused_hidden_dim": ("model", "fused_hidden_dim"),
|
||
"dropout": ("model", "dropout"),
|
||
"attn_dim": ("model", "attn_dim"),
|
||
"attn_heads": ("model", "attn_heads"),
|
||
"attn_layers": ("model", "attn_layers"),
|
||
"batch_size": ("train", "batch_size"),
|
||
"epochs": ("train", "epochs"),
|
||
"lr": ("train", "lr"),
|
||
"weight_decay": ("train", "weight_decay"),
|
||
"val_split": ("train", "val_split"),
|
||
"checkpoint_dir": ("train", "checkpoint_dir"),
|
||
"patience": ("train", "patience"),
|
||
"num_workers": ("train", "num_workers"),
|
||
"seed": ("train", "seed"),
|
||
"max_grad_norm": ("train", "max_grad_norm"),
|
||
"augment": ("train", "augment"),
|
||
"predict_delta": ("train", "predict_delta"),
|
||
"rollout_len": ("train", "rollout_len"),
|
||
"ss_prob": ("train", "ss_prob"),
|
||
}
|
||
args_dict = vars(args)
|
||
for arg_name, (section, field) in mapping.items():
|
||
val = args_dict.get(arg_name)
|
||
if val is not None:
|
||
if field == "column_weights" and isinstance(val, str):
|
||
val = [float(v) for v in val.split(",")]
|
||
setattr(getattr(cfg, section), field, val)
|
||
return cfg
|