feat(perception): prewarm RF-DETR before source admission

tests/test_rf_detr_detector_provider.py

@@ -74,9 +74,7 @@ class _Resizer:
     def __init__(self) -> None:
         self.source: NDArray[np.uint8] | None = None
 
-    def resize(
-        self, image: NDArray[np.uint8], width: int, height: int
-    ) -> NDArray[np.uint8]:
+    def resize(self, image: NDArray[np.uint8], width: int, height: int) -> NDArray[np.uint8]:
         self.source = image.copy()
         output = np.empty((height, width, 3), dtype=np.uint8)
         output[:, :, 0] = 255
@@ -135,9 +133,7 @@ def test_postprocess_maps_sparse_coco_slots_and_emits_only_risk_classes() -> Non
     assert tuple(item.label for item in result.detections) == ("person", "dog")
     assert result.detections[0].score == pytest.approx(0.8, abs=0.001)
     assert result.detections[1].score == pytest.approx(0.75, abs=0.001)
-    assert result.detections[1].bbox_xyxy == pytest.approx(
-        (300.0, 200.0, 500.0, 400.0), abs=0.03
-    )
+    assert result.detections[1].bbox_xyxy == pytest.approx((300.0, 200.0, 500.0, 400.0), abs=0.03)
     assert dict(result.rejected) == {"non-risk-class": 1, "unmapped-class-slot": 1}
 
     with pytest.raises(RfDetrDetectorError, match="tensor types"):
@@ -193,6 +189,30 @@ def test_shadow_provider_reports_preprocess_transport_and_postprocess_timing() -
     }
 
 
+def test_shadow_provider_warmup_is_idempotent_and_excluded_from_frame_counts() -> None:
+    backend = _Backend(_output())
+    provider = RfDetrShadowDetectorProvider(
+        mask=np.ones((600, 800), dtype=np.bool_),
+        backend=backend,
+        resizer=_Resizer(),
+        clock_ns=iter((10, 20, 50, 70)).__next__,
+    )
+
+    first = provider.warm_up()
+    second = provider.warm_up()
+
+    assert first is second
+    assert backend.calls == 1
+    assert first.completed is True
+    assert first.inference_passes == 1
+    assert first.preprocess_duration_ns == 10
+    assert first.inference_transport_duration_ns == 30
+    assert first.postprocess_duration_ns == 20
+    assert first.total_duration_ns == 60
+    assert provider.snapshot().input_frames == 0
+    assert provider.snapshot().completed_frames == 0
+
+
 def test_shadow_profile_is_fixed_and_transport_pins_model_version() -> None:
     assert RF_DETR_CONFIG.minimum_score == 0.25
     with pytest.raises(RfDetrDetectorError, match="cannot be tuned"):
@@ -209,16 +229,11 @@ def test_shadow_profile_is_fixed_and_transport_pins_model_version() -> None:
 
 def test_shadow_profile_pins_worker_engine_and_retains_false_authority() -> None:
     profile = json.loads(
-        (REPOSITORY_ROOT / "config/perception/rf-detr-large-risk-shadow-v0.json").read_text(
-            "utf-8"
-        )
+        (REPOSITORY_ROOT / "config/perception/rf-detr-large-risk-shadow-v0.json").read_text("utf-8")
     )
 
     assert profile["model"]["strongly_typed_fp16_onnx_sha256"] == RF_DETR_FP16_ONNX_SHA256
-    assert (
-        profile["model"]["worker_006_rtx4090_tensorrt_11_engine_sha256"]
-        == RF_DETR_ENGINE_SHA256
-    )
+    assert profile["model"]["worker_006_rtx4090_tensorrt_11_engine_sha256"] == RF_DETR_ENGINE_SHA256
     assert profile["emission"]["single_inference_per_source_frame"] is True
     assert profile["emission"]["geometry_owns_static_occupancy"] is True
     assert profile["emission"]["unlisted_semantic_classes_emitted"] is False