qing
823e91a30e
- hailo_infer: 帧归一化判定改为量化网格 dequant(0)（旧启发式把归一化 帧当原始像素喂入，图像路径饱和破坏；实机 max_diff 2.55→0.37， img 敏感性 1.44→22.26） - export_deploy: pool kernel 由卷积真实输出尺寸探测（H//16 在 1080 高度下静默丢弃末行）；mobilenet backbone 给出明确报错 - mt_parser: @Action 为文件末位块时解析成功，不再静默丢数据 - config: checkpoint train 段未知键/非 dict 校验；init-from 兼容性 警告助手（normalize/action_dim），train 与 pretrain 共用 - dataset: stats 文件形状校验；predict: 移除误导性 --action-dim - dfc_compile: --frame-count 参数化（原硬编码 3 帧） - gen_calibration: 旧模式校准图像改分块 memmap 生成（1080p 原一次性 分配 ~600MB） - _npu_test: torch 参考反归一化对齐后再与 HEF 比较 - 单测 81→90 全过；README 与测速报告更新为验证轮次数据
242 lines
10 KiB
Python
242 lines
10 KiB
Python
import argparse
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from dataclasses import dataclass, asdict, field
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from typing import Optional
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@dataclass
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class DataConfig:
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image_width: int = 1920
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image_height: int = 1080
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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 = False
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action_stats_path: Optional[str] = None
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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 1920×1080，batch=32 在 CPU/Pi 上内存不可行；
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# 高分辨率训练取小 batch，小分辨率可用 --batch-size 调大
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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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@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("--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("--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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("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 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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]
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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_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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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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"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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}
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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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setattr(getattr(cfg, section), field, val)
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return cfg
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