Update project and configurations

intelligent_cabin/archive/tests/test_multi_intent_detector.py

@@ -0,0 +1,144 @@
+from __future__ import annotations
+
+import unittest
+
+import torch
+
+from app.schemas.intent import IntentDefinition
+from app.services.multi_intent_detector import BertMultiIntentDetector, JointBertMultiIntentDetector
+
+
+class FakeTokenizer:
+    def __call__(self, text, truncation=True, padding=False, return_tensors="pt"):
+        _ = (text, truncation, padding, return_tensors)
+        return {
+            "input_ids": torch.tensor([[101, 102]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1]], dtype=torch.long),
+        }
+
+
+class FakeModel:
+    def __init__(self, logits: list[float], id2label: dict[int, str]) -> None:
+        self.config = type("Config", (), {"id2label": id2label})()
+        self._logits = torch.tensor([logits], dtype=torch.float32)
+
+    def eval(self) -> None:
+        return None
+
+    def __call__(self, **kwargs):
+        _ = kwargs
+        return type("Output", (), {"logits": self._logits})()
+
+
+class RuntimeBackedDetector(BertMultiIntentDetector):
+    def __init__(
+        self,
+        logits: list[float],
+        id2label: dict[int, str],
+        threshold: float = 0.45,
+        top_k: int = 8,
+        max_labels: int = 4,
+    ) -> None:
+        super().__init__(
+            model_path="unused",
+            threshold=threshold,
+            top_k=top_k,
+            max_labels=max_labels,
+        )
+        self._runtime = (torch, FakeTokenizer(), FakeModel(logits, id2label))
+
+    def _get_runtime(self):
+        return self._runtime
+
+
+class MultiIntentDetectorTests(unittest.TestCase):
+    def test_detector_filters_blocked_and_unknown_labels(self) -> None:
+        detector = RuntimeBackedDetector(
+            logits=[2.4, 2.0, 3.2, 2.6],
+            id2label={
+                0: "cabin_window_open",
+                1: "cabin_play_music",
+                2: "__social__",
+                3: "unknown_intent",
+            },
+            threshold=0.8,
+            top_k=4,
+        )
+        intents = [
+            IntentDefinition(intent_id="cabin_window_open", plugin_id="plugin.window", domain="cabin"),
+            IntentDefinition(intent_id="cabin_play_music", plugin_id="plugin.music", domain="cabin"),
+        ]
+
+        result = detector.detect("打开车窗并播放音乐", intents)
+
+        self.assertTrue(result.detected)
+        self.assertEqual(result.backend_name, "bert-multi-label")
+        self.assertEqual([item.intent_id for item in result.candidates], ["cabin_window_open", "cabin_play_music"])
+
+    def test_detector_respects_threshold_and_max_labels(self) -> None:
+        detector = RuntimeBackedDetector(
+            logits=[2.8, 2.5, 2.2],
+            id2label={
+                0: "cabin_window_open",
+                1: "cabin_play_music",
+                2: "cabin_nav_to",
+            },
+            threshold=0.89,
+            top_k=3,
+            max_labels=2,
+        )
+        intents = [
+            IntentDefinition(intent_id="cabin_window_open", plugin_id="plugin.window", domain="cabin"),
+            IntentDefinition(intent_id="cabin_play_music", plugin_id="plugin.music", domain="cabin"),
+            IntentDefinition(intent_id="cabin_nav_to", plugin_id="plugin.nav", domain="cabin"),
+        ]
+
+        result = detector.detect("开窗放歌去公司", intents)
+
+        self.assertTrue(result.detected)
+        self.assertEqual(len(result.candidates), 2)
+        self.assertEqual([item.intent_id for item in result.candidates], ["cabin_window_open", "cabin_play_music"])
+
+    def test_joint_bert_detector_wraps_shared_runtime(self) -> None:
+        intents = [
+            IntentDefinition(intent_id="cabin_window_open", plugin_id="plugin.window", domain="cabin"),
+            IntentDefinition(intent_id="cabin_play_music", plugin_id="plugin.music", domain="cabin"),
+        ]
+
+        class FakeJointNlu:
+            def __init__(self) -> None:
+                self.calls: list[dict[str, object]] = []
+
+            def predict_multi_intents(self, text, known_intents, threshold=0.45, max_labels=4, top_k=8):
+                self.calls.append(
+                    {
+                        "text": text,
+                        "threshold": threshold,
+                        "max_labels": max_labels,
+                        "top_k": top_k,
+                        "known_count": len(known_intents),
+                    }
+                )
+                return [
+                    type("Candidate", (), {"intent_id": "cabin_window_open", "score": 0.93})(),
+                    type("Candidate", (), {"intent_id": "cabin_play_music", "score": 0.88})(),
+                ]
+
+            def warmup(self, sample_text="") -> bool:
+                _ = sample_text
+                return True
+
+        fake_nlu = FakeJointNlu()
+        detector = JointBertMultiIntentDetector(fake_nlu, threshold=0.5, top_k=6, max_labels=3)
+
+        result = detector.detect("打开车窗并播放音乐", intents)
+
+        self.assertTrue(result.detected)
+        self.assertEqual(result.backend_name, "joint-bert-multi-label")
+        self.assertEqual([item.intent_id for item in result.candidates], ["cabin_window_open", "cabin_play_music"])
+        self.assertEqual(fake_nlu.calls[0]["threshold"], 0.5)
+        self.assertEqual(fake_nlu.calls[0]["top_k"], 6)
+
+
+if __name__ == "__main__":
+    unittest.main()