Align task API and add FunCaptcha support

inference/pipeline.py

@@ -26,6 +26,7 @@ from config import (
     REGRESSION_RANGE,
 )
 from inference.math_eval import eval_captcha_math
+from inference.model_metadata import load_model_metadata
 
 
 def _try_import_ort():
@@ -66,6 +67,11 @@ class CaptchaPipeline:
             "math": MATH_CHARS,
             "3d_text": THREED_CHARS,
         }
+        self._classifier_class_names = tuple(CAPTCHA_TYPES)
+        self._regression_ranges = {
+            "3d_rotate": REGRESSION_RANGE["3d_rotate"],
+            "3d_slider": REGRESSION_RANGE["3d_slider"],
+        }
 
         # 回归模型类型
         self._regression_types = {"3d_rotate", "3d_slider"}
@@ -86,6 +92,7 @@ class CaptchaPipeline:
         opts.intra_op_num_threads = 2
 
         self._sessions: dict[str, "ort.InferenceSession"] = {}
+        self._metadata: dict[str, dict] = {}
         for name, fname in self._model_files.items():
             path = self.models_dir / fname
             if path.exists():
@@ -93,6 +100,7 @@ class CaptchaPipeline:
                     str(path), sess_options=opts,
                     providers=["CPUExecutionProvider"],
                 )
+                self._metadata[name] = load_model_metadata(path) or {}
 
         loaded = list(self._sessions.keys())
         if not loaded:
@@ -135,7 +143,14 @@ class CaptchaPipeline:
         input_name = session.get_inputs()[0].name
         logits = session.run(None, {input_name: inp})[0]  # (1, num_types)
         idx = int(np.argmax(logits, axis=1)[0])
-        return CAPTCHA_TYPES[idx]
+        class_names = tuple(
+            self._metadata.get("classifier", {}).get("class_names", self._classifier_class_names)
+        )
+        if idx >= len(class_names):
+            raise RuntimeError(
+                f"分类器输出索引越界: idx={idx}, classes={len(class_names)}"
+            )
+        return class_names[idx]
 
     def solve(
         self,
@@ -182,14 +197,17 @@ class CaptchaPipeline:
             # 回归模型: 输出 (batch, 1) sigmoid 值
             output = session.run(None, {input_name: inp})[0]  # (1, 1)
             sigmoid_val = float(output[0, 0])
-            lo, hi = REGRESSION_RANGE[captcha_type]
+            lo, hi = self._metadata.get(captcha_type, {}).get(
+                "label_range",
+                self._regression_ranges[captcha_type],
+            )
             real_val = sigmoid_val * (hi - lo) + lo
             raw_text = f"{real_val:.1f}"
             result = str(int(round(real_val)))
         else:
             # CTC 模型
             logits = session.run(None, {input_name: inp})[0]  # (T, 1, C)
-            chars = self._chars[captcha_type]
+            chars = self._metadata.get(captcha_type, {}).get("chars", self._chars[captcha_type])
             raw_text = self._ctc_greedy_decode(logits, chars)
 
             # 后处理