Align task API and add FunCaptcha support

tests/test_data_fingerprint.py

@@ -0,0 +1,109 @@
+"""
+测试合成数据指纹与 ONNX metadata 辅助逻辑。
+"""
+
+from pathlib import Path
+
+from PIL import Image
+
+from inference.model_metadata import load_model_metadata, write_model_metadata
+from training.data_fingerprint import (
+    build_dataset_spec,
+    ensure_synthetic_dataset,
+    load_dataset_manifest,
+)
+
+
+class AdoptableGenerator:
+    def generate_dataset(self, num_samples: int, output_dir: str):
+        out_dir = Path(output_dir)
+        out_dir.mkdir(parents=True, exist_ok=True)
+        for idx in range(num_samples):
+            Image.new("L", (2, 2), color=255).save(out_dir / f"kept_{idx:06d}.png")
+
+
+class RefreshingGenerator:
+    def generate_dataset(self, num_samples: int, output_dir: str):
+        out_dir = Path(output_dir)
+        out_dir.mkdir(parents=True, exist_ok=True)
+        for idx in range(num_samples):
+            Image.new("L", (2, 2), color=255).save(out_dir / f"20÷4_{idx:06d}.png")
+
+
+class TestDataFingerprint:
+    def test_adopt_existing_dataset_when_manifest_missing(self, tmp_path):
+        dataset_dir = tmp_path / "normal"
+        dataset_dir.mkdir()
+        for idx, label in enumerate(["AB12", "CD34"]):
+            Image.new("L", (2, 2), color=255).save(dataset_dir / f"{label}_{idx:06d}.png")
+
+        state = ensure_synthetic_dataset(
+            dataset_dir,
+            generator_cls=AdoptableGenerator,
+            spec=build_dataset_spec(
+                AdoptableGenerator,
+                config_key="normal",
+                config_snapshot={"chars": "ABCD1234"},
+            ),
+            gen_count=2,
+            exact_count=2,
+            adopt_if_missing=True,
+        )
+
+        manifest = load_dataset_manifest(dataset_dir)
+        assert state["adopted"] is True
+        assert state["refreshed"] is False
+        assert manifest is not None
+        assert manifest["adopted_existing"] is True
+        assert manifest["sample_count"] == 2
+
+    def test_refresh_dataset_when_validator_fails(self, tmp_path):
+        dataset_dir = tmp_path / "math"
+        dataset_dir.mkdir()
+        for idx, label in enumerate(["1+1", "2-1"]):
+            Image.new("L", (2, 2), color=255).save(dataset_dir / f"{label}_{idx:06d}.png")
+
+        state = ensure_synthetic_dataset(
+            dataset_dir,
+            generator_cls=RefreshingGenerator,
+            spec=build_dataset_spec(
+                RefreshingGenerator,
+                config_key="math",
+                config_snapshot={"operators": ["+", "-", "÷"]},
+            ),
+            gen_count=2,
+            exact_count=2,
+            validator=lambda files: any("÷" in path.stem for path in files),
+            adopt_if_missing=True,
+        )
+
+        files = sorted(dataset_dir.glob("*.png"))
+        manifest = load_dataset_manifest(dataset_dir)
+        assert state["refreshed"] is True
+        assert state["adopted"] is False
+        assert manifest is not None
+        assert manifest["adopted_existing"] is False
+        assert len(files) == 2
+        assert all("÷" in path.stem for path in files)
+
+
+class TestModelMetadata:
+    def test_write_and_load_model_metadata(self, tmp_path):
+        model_path = tmp_path / "normal.onnx"
+        model_path.touch()
+
+        write_model_metadata(
+            model_path,
+            {
+                "model_name": "normal",
+                "task": "ctc",
+                "chars": "ABC",
+                "input_shape": [1, 40, 120],
+            },
+        )
+
+        metadata = load_model_metadata(model_path)
+        assert metadata is not None
+        assert metadata["version"] == 1
+        assert metadata["chars"] == "ABC"
+        assert metadata["task"] == "ctc"