feat(perception): add RF-DETR upstream parity gate

tests/test_m48t_upstream_parity.py

@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import importlib.util
+import json
+from pathlib import Path
+from types import ModuleType
+
+import numpy as np
+
+REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
+RUNNER_PATH = REPOSITORY_ROOT / "experiments/perception/run_m48t_upstream_parity_worker.py"
+PROFILE_PATH = REPOSITORY_ROOT / "config/perception/m48t-upstream-parity-v1.json"
+
+
+def load_runner() -> ModuleType:
+    specification = importlib.util.spec_from_file_location("m48t_upstream_parity", RUNNER_PATH)
+    assert specification is not None and specification.loader is not None
+    module = importlib.util.module_from_spec(specification)
+    specification.loader.exec_module(module)
+    return module
+
+
+def test_upstream_parity_profile_freezes_official_and_deployed_providers() -> None:
+    profile = json.loads(PROFILE_PATH.read_text("utf-8"))
+
+    assert profile["model"]["package_version"] == "1.9.4"
+    assert profile["model"]["resolution"] == [704, 704]
+    assert profile["dataset"]["image_count"] == 5000
+    assert profile["providers"]["pytorch"]["confidence_prefilter"] == 0.0
+    assert profile["providers"]["tensorrt"]["confidence_prefilter"] == 0.0
+    assert profile["authority"]["navigation_or_safety_accepted"] is False
+
+
+def test_tensorrt_decoder_uses_sparse_coco_ids_and_original_image_geometry() -> None:
+    runner = load_runner()
+    boxes = np.zeros((1, 300, 4), dtype=np.float16)
+    logits = np.full((1, 300, 91), -20, dtype=np.float16)
+    boxes[0, 4] = np.asarray((0.5, 0.5, 0.2, 0.4), dtype=np.float16)
+    logits[0, 4, 1] = np.float16(4.0)  # COCO sparse id 1: person
+    logits[0, 7, 12] = np.float16(5.0)  # sparse gap: must never escape
+
+    rows = runner.decode_tensorrt_coco_rows(
+        image_id=42,
+        image_width=1000,
+        image_height=500,
+        boxes=boxes,
+        logits=logits,
+        maximum_detections=2,
+    )
+
+    assert len(rows) == 1
+    assert rows[0]["image_id"] == 42
+    assert rows[0]["category_id"] == 1
+    assert np.allclose(rows[0]["bbox"], [400.0244, 149.9756, 199.9512, 200.0488], atol=0.1)
+
+
+def test_parity_decision_localizes_upstream_and_deployment_failures() -> None:
+    runner = load_runner()
+    profile = json.loads(PROFILE_PATH.read_text("utf-8"))
+
+    passed = runner.build_parity_decision(
+        profile=profile,
+        pytorch_metrics={"ap_50_95": 0.565, "ap_50": 0.751},
+        tensorrt_metrics={"ap_50_95": 0.563, "ap_50": 0.749},
+        full_admission_run=True,
+    )
+    assert passed["passed"] is True
+    assert passed["diagnosis"] == "upstream-and-tensorrt-parity-passed"
+
+    deployment_failure = runner.build_parity_decision(
+        profile=profile,
+        pytorch_metrics={"ap_50_95": 0.565, "ap_50": 0.751},
+        tensorrt_metrics={"ap_50_95": 0.54, "ap_50": 0.72},
+        full_admission_run=True,
+    )
+    assert deployment_failure["passed"] is False
+    assert deployment_failure["diagnosis"] == "tensorrt-deployment-parity-failed"
+
+    smoke = runner.build_parity_decision(
+        profile=profile,
+        pytorch_metrics={"ap_50_95": 0.565, "ap_50": 0.751},
+        tensorrt_metrics={"ap_50_95": 0.565, "ap_50": 0.751},
+        full_admission_run=False,
+    )
+    assert smoke["passed"] is False
+    assert smoke["diagnosis"] == "bounded-smoke-only"