feat: stage7 — Model Manager (F16) and Sequential VO (F07)

tests/test_models.py

@@ -0,0 +1,49 @@
+"""Tests for Model Manager (F16)."""
+
+import numpy as np
+from gps_denied.core.models import ModelManager
+
+def test_load_and_get_model():
+    manager = ModelManager()
+    
+    # Should auto-load
+    engine = manager.get_inference_engine("SuperPoint")
+    assert engine.model_name == "SuperPoint"
+    
+    # Check fallback/dummy
+    assert manager.fallback_to_onnx("SuperPoint") is True
+    assert manager.optimize_to_tensorrt("SuperPoint", "path.onnx") == "path.onnx.trt"
+
+
+def test_mock_superpoint():
+    manager = ModelManager()
+    engine = manager.get_inference_engine("SuperPoint")
+    
+    dummy_img = np.zeros((480, 640, 3), dtype=np.uint8)
+    res = engine.infer(dummy_img)
+    
+    assert "keypoints" in res
+    assert "descriptors" in res
+    assert "scores" in res
+    assert len(res["keypoints"]) == 500
+    assert res["descriptors"].shape == (500, 256)
+
+
+def test_mock_lightglue():
+    manager = ModelManager()
+    engine = manager.get_inference_engine("LightGlue")
+    
+    # Need mock features
+    class DummyF:
+        def __init__(self, keypoints):
+            self.keypoints = keypoints
+            
+    f1 = DummyF(np.random.rand(120, 2))
+    f2 = DummyF(np.random.rand(150, 2))
+    
+    res = engine.infer({"features1": f1, "features2": f2})
+    
+    assert "matches" in res
+    assert len(res["matches"]) == 100  # min(100, 120, 150)
+    assert res["keypoints1"].shape == (100, 2)
+    assert res["keypoints2"].shape == (100, 2)