qing
73b72d6889
- train/val 归一化统计量改为仅用训练集计算并落盘复用，修复 val loss 不可信 - predict.py 统计文件路径改用 os.path 处理，修复跨平台与无分隔符路径问题 - dataset.py jpg/png 混合时统一按文件名排序，保证时间顺序 - 新增 --seed 全局随机种子（含 DataLoader generator），训练可复现 - 新增 --max-grad-norm 梯度裁剪（默认 1.0），防 GRU 梯度爆炸 - get_episode_count 与数据集目录过滤条件对齐，避免无效目录导致索引错位 - 空验证集返回 inf 并保底保存最终权重，避免伪造 best 模型 - 清理 export_onnx.py 重复分支死代码
117 lines
4.4 KiB
Python
117 lines
4.4 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_size: int = 64
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action_dim: int = 8
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history_len: int = 5
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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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action_hidden_dim: int = 64
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fused_hidden_dim: int = 128
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dropout: float = 0.1
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history_encoder: str = "mlp"
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@dataclass
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class TrainConfig:
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batch_size: int = 32
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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-size", 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("--data-dir", type=str, default=None)
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parser.add_argument("--normalize-actions", action="store_true", 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("--action-hidden-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("--history-encoder", type=str, default=None, choices=["mlp", "gru"])
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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("--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 apply_overrides(cfg: Config, args: argparse.Namespace) -> Config:
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mapping = {
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"image_size": ("data", "image_size"),
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"action_dim": ("data", "action_dim"),
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"history_len": ("data", "history_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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"action_hidden_dim": ("model", "action_hidden_dim"),
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"fused_hidden_dim": ("model", "fused_hidden_dim"),
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"dropout": ("model", "dropout"),
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"history_encoder": ("model", "history_encoder"),
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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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