refactor: Config序列化统一BaseConfig基类

- 新增astrai/config/base.py，提供to_dict/from_dict基类
- 统一命名：load/save → from_file/to_file
- Checkpoint.meta合并训练配置到meta.json
- sys.stderr.warn → warnings.warn
- from_file改为classmethod

astrai/config/base.py

@@ -0,0 +1,77 @@
+import json
+from dataclasses import MISSING, dataclass, fields
+from typing import Any, Dict, Optional, Self, get_type_hints
+
+
+@dataclass
+class BaseConfig:
+    def to_dict(self) -> Dict[str, Any]:
+        d = {}
+        for fld in fields(self):
+            v = getattr(self, fld.name)
+            if isinstance(v, (str, int, float, bool)):
+                d[fld.name] = v
+            elif v is None:
+                d[fld.name] = None
+            elif isinstance(v, dict):
+                try:
+                    json.dumps(v)
+                    d[fld.name] = v
+                except (TypeError, ValueError):
+                    pass
+        return d
+
+    @classmethod
+    def from_dict(cls, d: Dict[str, Any]) -> Self:
+        hints = get_type_hints(cls)
+        inst = cls.__new__(cls)
+        for fld in fields(cls):
+            if fld.name in d:
+                v = d[fld.name]
+                target = cls._unwrap_optional(hints.get(fld.name))
+                if target is not None:
+                    try:
+                        v = cls._coerce(v, target)
+                    except (TypeError, ValueError):
+                        pass
+                object.__setattr__(inst, fld.name, v)
+            elif fld.default is not MISSING:
+                object.__setattr__(inst, fld.name, fld.default)
+            elif fld.default_factory is not MISSING:
+                object.__setattr__(inst, fld.name, fld.default_factory())
+            else:
+                object.__setattr__(inst, fld.name, None)
+        return inst
+
+    @staticmethod
+    def _unwrap_optional(tp) -> Optional[type]:
+        if tp is None:
+            return None
+        origin = getattr(tp, "__origin__", None)
+        if origin is not None:
+            args = getattr(tp, "__args__", ())
+            non_none = [a for a in args if a is not type(None)]
+            return non_none[0] if non_none else None
+        return tp
+
+    @staticmethod
+    def _coerce(value: Any, target_type: type) -> Any:
+        if target_type is bool and isinstance(value, bool):
+            return value
+        if (
+            target_type is int
+            and isinstance(value, (int, float))
+            and not isinstance(value, bool)
+        ):
+            return int(value)
+        if (
+            target_type is float
+            and isinstance(value, (int, float))
+            and not isinstance(value, bool)
+        ):
+            return float(value)
+        if target_type is str and isinstance(value, str):
+            return value
+        if isinstance(value, target_type):
+            return value
+        raise TypeError

astrai/config/model_config.py

@@ -1,12 +1,14 @@
 import json
-import sys
+import warnings
 from dataclasses import dataclass, fields
-from typing import Any, Dict, Optional, Self, get_type_hints
+from typing import Any, Dict, Optional, Self
+
+from astrai.config.base import BaseConfig
 
 
 @dataclass
-class BaseModelConfig:
-    """Field-aware JSON load/save for dataclass configs.
+class BaseModelConfig(BaseConfig):
+    """Field-aware JSON from/to file for dataclass configs.
 
     Subclass with additional fields. The base ``model_type`` field
     enables ``AutoModel`` to pick the correct subclass.
@@ -14,76 +16,25 @@ class BaseModelConfig:
 
     model_type: Optional[str] = None
 
-    def load(self, config_path: str) -> Self:
-        raw: Dict[str, Any] = {}
+    @classmethod
+    def from_file(cls, config_path: str) -> Self:
         with open(config_path, "r") as f:
-            raw.update(json.load(f))
+            raw: Dict[str, Any] = json.load(f)
 
-        hints = get_type_hints(type(self))
-        valid = {fld.name for fld in fields(self)}
-        for key, value in raw.items():
+        valid = {fld.name for fld in fields(cls)}
+        for key in list(raw):
             if key not in valid:
-                sys.stderr.write(f"WARNING: unknown config key '{key}'\n")
-                continue
+                warnings.warn(f"Unknown config key '{key}'")
+                del raw[key]
 
-            target_type = self._unwrap_optional(hints.get(key))
-            if target_type is None:
-                continue
+        return cls.from_dict(raw)
 
-            try:
-                value = self._coerce(value, target_type)
-            except (TypeError, ValueError):
-                sys.stderr.write(
-                    f"WARNING: cannot coerce '{key}' = {value!r} to {target_type}\n"
-                )
-                continue
-
-            setattr(self, key, value)
-
-        return self
-
-    def save(self, config_path: str):
-        config_dict: Dict[str, Any] = {}
-        for fld in fields(self):
-            v = getattr(self, fld.name)
-            if v is not None:
-                config_dict[fld.name] = v
+    def to_file(self, config_path: str):
+        d = self.to_dict()
+        config_dict = {k: v for k, v in d.items() if v is not None}
         with open(config_path, "w") as f:
             json.dump(config_dict, f, indent=4)
 
-    @staticmethod
-    def _unwrap_optional(tp: type) -> Optional[type]:
-        if tp is None:
-            return None
-        origin = getattr(tp, "__origin__", None)
-        if origin is not None:
-            args = getattr(tp, "__args__", ())
-            non_none = [a for a in args if a is not type(None)]
-            return non_none[0] if non_none else None
-        return tp
-
-    @staticmethod
-    def _coerce(value: Any, target_type: type) -> Any:
-        if target_type is bool and isinstance(value, bool):
-            return value
-        if (
-            target_type is int
-            and isinstance(value, (int, float))
-            and not isinstance(value, bool)
-        ):
-            return int(value)
-        if (
-            target_type is float
-            and isinstance(value, (int, float))
-            and not isinstance(value, bool)
-        ):
-            return float(value)
-        if target_type is str and isinstance(value, str):
-            return value
-        if isinstance(value, target_type):
-            return value
-        raise TypeError
-
 
 @dataclass
 class ModelConfig(BaseModelConfig):

astrai/config/train_config.py

@@ -6,9 +6,11 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import Dataset
 
+from astrai.config.base import BaseConfig
+
 
 @dataclass
-class TrainConfig:
+class TrainConfig(BaseConfig):
     # basic setting
     model: nn.Module = field(default=None, metadata={"help": "Model for training."})
     strategy: str = field(default=None, metadata={"help": "Training strategy."})