Align task API and add FunCaptcha support

training/train_classifier.py

@@ -23,6 +23,11 @@ from config import (
     NUM_CAPTCHA_TYPES,
     IMAGE_SIZE,
     TRAIN_CONFIG,
+    GENERATE_CONFIG,
+    NORMAL_CHARS,
+    MATH_CHARS,
+    THREED_CHARS,
+    REGRESSION_RANGE,
     CLASSIFIER_DIR,
     SYNTHETIC_NORMAL_DIR,
     SYNTHETIC_MATH_DIR,
@@ -40,7 +45,13 @@ from generators.math_gen import MathCaptchaGenerator
 from generators.threed_gen import ThreeDCaptchaGenerator
 from generators.threed_rotate_gen import ThreeDRotateGenerator
 from generators.threed_slider_gen import ThreeDSliderGenerator
+from inference.model_metadata import write_model_metadata
 from models.classifier import CaptchaClassifier
+from training.data_fingerprint import (
+    build_dataset_spec,
+    ensure_synthetic_dataset,
+    labels_cover_tokens,
+)
 from training.dataset import CaptchaDataset, build_train_transform, build_val_transform
 
 
@@ -63,27 +74,55 @@ def _prepare_classifier_data():
         ("3d_rotate", SYNTHETIC_3D_ROTATE_DIR, ThreeDRotateGenerator),
         ("3d_slider", SYNTHETIC_3D_SLIDER_DIR, ThreeDSliderGenerator),
     ]
+    chars_map = {
+        "normal": NORMAL_CHARS,
+        "math": MATH_CHARS,
+        "3d_text": THREED_CHARS,
+    }
 
     for cls_name, syn_dir, gen_cls in type_info:
         syn_dir = Path(syn_dir)
-        existing = sorted(syn_dir.glob("*.png"))
+        config_snapshot = {
+            "generate_config": GENERATE_CONFIG[cls_name],
+            "image_size": IMAGE_SIZE[cls_name],
+        }
+        if cls_name in chars_map:
+            config_snapshot["chars"] = chars_map[cls_name]
+        if cls_name in REGRESSION_RANGE:
+            config_snapshot["label_range"] = REGRESSION_RANGE[cls_name]
 
-        # 如果合成数据不够，生成一些
-        if len(existing) < per_class:
-            print(f"[数据] {cls_name} 合成数据不足 ({len(existing)}/{per_class})，开始生成...")
-            gen = gen_cls()
-            gen.generate_dataset(per_class, str(syn_dir))
-            existing = sorted(syn_dir.glob("*.png"))
+        validator = None
+        if cls_name == "math":
+            required_ops = tuple(GENERATE_CONFIG["math"]["operators"])
+            validator = lambda files, tokens=required_ops: labels_cover_tokens(files, tokens)
+
+        dataset_state = ensure_synthetic_dataset(
+            syn_dir,
+            generator_cls=gen_cls,
+            spec=build_dataset_spec(
+                gen_cls,
+                config_key=cls_name,
+                config_snapshot=config_snapshot,
+            ),
+            gen_count=per_class,
+            min_count=per_class,
+            validator=validator,
+            adopt_if_missing=cls_name in {"normal", "math"},
+        )
+        if dataset_state["refreshed"]:
+            print(f"[数据] {cls_name} 合成数据已刷新: {dataset_state['sample_count']} 张")
+        elif dataset_state["adopted"]:
+            print(f"[数据] {cls_name} 合成数据已采纳并写入指纹: {dataset_state['sample_count']} 张")
+        else:
+            print(f"[数据] {cls_name} 合成数据已就绪: {dataset_state['sample_count']} 张")
+
+        existing = sorted(syn_dir.glob("*.png"))
 
         # 复制到 classifier 目录
         cls_dir = CLASSIFIER_DIR / cls_name
         cls_dir.mkdir(parents=True, exist_ok=True)
-        already = len(list(cls_dir.glob("*.png")))
-        if already >= per_class:
-            print(f"[数据] {cls_name} 分类器数据已就绪: {already} 张")
-            continue
 
-        # 清空后重新链接
+        # classifier 数据是派生目录，每次重建以对齐当前源数据与指纹状态。
         for f in cls_dir.glob("*.png"):
             f.unlink()
 
@@ -239,6 +278,15 @@ def main():
         if ONNX_CONFIG["dynamic_batch"]
         else None,
     )
+    write_model_metadata(
+        onnx_path,
+        {
+            "model_name": "classifier",
+            "task": "classifier",
+            "class_names": list(CAPTCHA_TYPES),
+            "input_shape": [1, img_h, img_w],
+        },
+    )
     print(f"[ONNX] 导出完成: {onnx_path}  ({onnx_path.stat().st_size / 1024:.1f} KB)")
 
     return best_acc