Align task API and add FunCaptcha support

tests/test_pipeline.py

@@ -8,11 +8,15 @@
 """
 
 import math
+from pathlib import Path
 
 import numpy as np
 import pytest
+from PIL import Image
 
 from inference.math_eval import eval_captcha_math
+from inference.model_metadata import write_model_metadata
+import inference.pipeline as pipeline_module
 from inference.pipeline import CaptchaPipeline
 
 
@@ -97,6 +101,96 @@ class TestCTCGreedyDecode:
         assert result == "AA"
 
 
+class _FakeInput:
+    name = "input"
+
+
+class _FakeSession:
+    def __init__(self, path, *args, **kwargs):
+        self.model_name = Path(path).name
+
+    def get_inputs(self):
+        return [_FakeInput()]
+
+    def run(self, output_names, feed_dict):
+        if self.model_name == "classifier.onnx":
+            return [np.array([[0.1, 0.9]], dtype=np.float32)]
+        if self.model_name == "normal.onnx":
+            logits = np.full((2, 1, 4), -10.0, dtype=np.float32)
+            logits[0, 0, 2] = 10.0
+            logits[1, 0, 0] = 10.0
+            return [logits]
+        if self.model_name == "threed_rotate.onnx":
+            return [np.array([[0.25]], dtype=np.float32)]
+        raise AssertionError(f"unexpected fake session: {self.model_name}")
+
+
+class _FakeSessionOptions:
+    def __init__(self):
+        self.inter_op_num_threads = 0
+        self.intra_op_num_threads = 0
+
+
+class _FakeOrt:
+    SessionOptions = _FakeSessionOptions
+    InferenceSession = _FakeSession
+
+
+class TestPipelineMetadata:
+    def test_classifier_uses_metadata_class_order(self, tmp_path, monkeypatch):
+        (tmp_path / "classifier.onnx").touch()
+        write_model_metadata(
+            tmp_path / "classifier.onnx",
+            {
+                "model_name": "classifier",
+                "task": "classifier",
+                "class_names": ["math", "normal"],
+                "input_shape": [1, 64, 128],
+            },
+        )
+        monkeypatch.setattr(pipeline_module, "_try_import_ort", lambda: _FakeOrt)
+
+        pipeline = CaptchaPipeline(models_dir=tmp_path)
+        captcha_type = pipeline.classify(Image.new("RGB", (32, 32), color="white"))
+        assert captcha_type == "normal"
+
+    def test_solve_uses_ctc_chars_metadata(self, tmp_path, monkeypatch):
+        (tmp_path / "normal.onnx").touch()
+        write_model_metadata(
+            tmp_path / "normal.onnx",
+            {
+                "model_name": "normal",
+                "task": "ctc",
+                "chars": "XYZ",
+                "input_shape": [1, 40, 120],
+            },
+        )
+        monkeypatch.setattr(pipeline_module, "_try_import_ort", lambda: _FakeOrt)
+
+        pipeline = CaptchaPipeline(models_dir=tmp_path)
+        result = pipeline.solve(Image.new("RGB", (32, 32), color="white"), captcha_type="normal")
+        assert result["raw"] == "Y"
+        assert result["result"] == "Y"
+
+    def test_solve_uses_regression_label_range_metadata(self, tmp_path, monkeypatch):
+        (tmp_path / "threed_rotate.onnx").touch()
+        write_model_metadata(
+            tmp_path / "threed_rotate.onnx",
+            {
+                "model_name": "threed_rotate",
+                "task": "regression",
+                "label_range": [100, 200],
+                "input_shape": [1, 80, 80],
+            },
+        )
+        monkeypatch.setattr(pipeline_module, "_try_import_ort", lambda: _FakeOrt)
+
+        pipeline = CaptchaPipeline(models_dir=tmp_path)
+        result = pipeline.solve(Image.new("RGB", (32, 32), color="white"), captcha_type="3d_rotate")
+        assert result["raw"] == "125.0"
+        assert result["result"] == "125"
+
+
 # ============================================================
 # SlideSolver 测试
 # ============================================================