subnet_bridge: copy winning miner repo into library

README.md

@@ -8,19 +8,13 @@ tags:
 - object:truck
 - object:motorcycle
 - object:van
+
 manako:
   source: winner_fetch
   manifest_element_name: manak0/Detect-Vehicle
   winner_repo_id: danieldushku/s
   winner_revision: 52f7ac2a3342a57a9dce79159ea94b831d55de8d
-  score:
-    size_mb: 9.888267
-    performance_map50: 0.7555753708996942
-    E: 0.0764113035074492
-  last_benchmark:
-    type: synthetic_fixed
-    ran_at: '2026-10-26T08:39:39.426218Z'
-    result_path: benchmark/synthetic/bfb9a039-8a71-4ab5-9097-117d87572930.json
+  note: E=0.06073950 (map50=0.600000, size_mb=9.878250)
 ---
 
 # Detect-Vehicle-winner

benchmark/synthetic/bfb9a039-8a71-4ab5-9097-117d87572930.json

@@ -1,171 +0,0 @@
-{
-  "meta": {
-    "element_id": "manako/Detect-Vehicle-winner",
-    "run_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "benchmark_type": "synthetic_fixed",
-    "saved_at": "2026-10-26T08:39:39.426218Z",
-    "result_path": "benchmark/synthetic/bfb9a039-8a71-4ab5-9097-117d87572930.json",
-    "split_ref": "private_final"
-  },
-  "results": {
-    "overall_iou": 0.711301418417662,
-    "per_class_iou": {
-      "car": 0.7070302145582933,
-      "bus": 0.5482735465755711,
-      "truck": 0.5751458422637304,
-      "motorcycle": 0.5297182473130839,
-      "van": 0.0
-    },
-    "map_50": 0.7555753708996942,
-    "map_50_90": 0.579080216734731,
-    "precision": 0.9343807763401109,
-    "recall": 0.7567365269461078,
-    "per_class_map_50": {
-      "bus": 0.9262408088235294,
-      "car": 0.7609370193011648,
-      "motorcycle": 0.6351600115463183,
-      "truck": 0.6999636439277644,
-      "van": 0.0
-    },
-    "per_class_map_50_90": {
-      "bus": 0.8002013873465061,
-      "car": 0.5558016885440995,
-      "motorcycle": 0.3591731876237153,
-      "truck": 0.6011446034246029,
-      "van": 0.0
-    },
-    "per_class_precision": {
-      "bus": 0.984375,
-      "car": 0.951530612244898,
-      "motorcycle": 0.792,
-      "truck": 0.944954128440367,
-      "van": 0.0
-    },
-    "per_class_recall": {
-      "bus": 0.9264705882352942,
-      "car": 0.7659137577002053,
-      "motorcycle": 0.673469387755102,
-      "truck": 0.7006802721088435,
-      "van": 0.0
-    },
-    "adaptive_map_50": 0.7846899619936487,
-    "ffpi": 0.4835164835164835,
-    "false_positive_score": 0.9516483516483516,
-    "foundry_score": 0.8514733178555298,
-    "composite_score": 0.8514733178555298,
-    "scoring_policy": "subnet-style-composite-v1",
-    "matched_images": 91,
-    "gt_count": 1336,
-    "pred_count": 1082,
-    "classes": [
-      "car",
-      "bus",
-      "truck",
-      "motorcycle",
-      "van"
-    ],
-    "evaluated_element_revision": null,
-    "dataset_id": "7f5bf8d2-bf7a-4ecc-9d2f-b1071588f225",
-    "dataset_version_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "run_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "split_ref": "private_final",
-    "selected_frame_count": 91,
-    "selected_frame_indices": [
-      4,
-      8,
-      9,
-      12,
-      18,
-      22,
-      23,
-      26,
-      33,
-      34,
-      40,
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-      50,
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-      141,
-      144,
-      146,
-      148,
-      149,
-      154,
-      155,
-      156,
-      161,
-      164,
-      167,
-      169,
-      177,
-      184,
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-      193,
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-      201,
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-      356,
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-      374,
-      380,
-      387,
-      389,
-      393,
-      403
-    ],
-    "evaluated_version_ids": [
-      "bfb9a039-8a71-4ab5-9097-117d87572930"
-    ],
-    "skipped_missing_pairs": 0
-  }
-}

benchmark/synthetic/index.json

@@ -1,16 +0,0 @@
-[
-  {
-    "run_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "saved_at": "2026-10-26T08:39:39.426218Z",
-    "result_path": "benchmark/synthetic/bfb9a039-8a71-4ab5-9097-117d87572930.json",
-    "overall_iou": 0.711301418417662,
-    "map_50": 0.7555753708996942,
-    "map_50_90": 0.579080216734731,
-    "precision": 0.9343807763401109,
-    "recall": 0.7567365269461078,
-    "matched_images": 91,
-    "gt_count": 1336,
-    "pred_count": 1082,
-    "split_ref": "private_final"
-  }
-]

benchmark/synthetic/latest.json

@@ -1,171 +0,0 @@
-{
-  "meta": {
-    "element_id": "manako/Detect-Vehicle-winner",
-    "run_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "benchmark_type": "synthetic_fixed",
-    "saved_at": "2026-10-26T08:39:39.426218Z",
-    "result_path": "benchmark/synthetic/bfb9a039-8a71-4ab5-9097-117d87572930.json",
-    "split_ref": "private_final"
-  },
-  "results": {
-    "overall_iou": 0.711301418417662,
-    "per_class_iou": {
-      "car": 0.7070302145582933,
-      "bus": 0.5482735465755711,
-      "truck": 0.5751458422637304,
-      "motorcycle": 0.5297182473130839,
-      "van": 0.0
-    },
-    "map_50": 0.7555753708996942,
-    "map_50_90": 0.579080216734731,
-    "precision": 0.9343807763401109,
-    "recall": 0.7567365269461078,
-    "per_class_map_50": {
-      "bus": 0.9262408088235294,
-      "car": 0.7609370193011648,
-      "motorcycle": 0.6351600115463183,
-      "truck": 0.6999636439277644,
-      "van": 0.0
-    },
-    "per_class_map_50_90": {
-      "bus": 0.8002013873465061,
-      "car": 0.5558016885440995,
-      "motorcycle": 0.3591731876237153,
-      "truck": 0.6011446034246029,
-      "van": 0.0
-    },
-    "per_class_precision": {
-      "bus": 0.984375,
-      "car": 0.951530612244898,
-      "motorcycle": 0.792,
-      "truck": 0.944954128440367,
-      "van": 0.0
-    },
-    "per_class_recall": {
-      "bus": 0.9264705882352942,
-      "car": 0.7659137577002053,
-      "motorcycle": 0.673469387755102,
-      "truck": 0.7006802721088435,
-      "van": 0.0
-    },
-    "adaptive_map_50": 0.7846899619936487,
-    "ffpi": 0.4835164835164835,
-    "false_positive_score": 0.9516483516483516,
-    "foundry_score": 0.8514733178555298,
-    "composite_score": 0.8514733178555298,
-    "scoring_policy": "subnet-style-composite-v1",
-    "matched_images": 91,
-    "gt_count": 1336,
-    "pred_count": 1082,
-    "classes": [
-      "car",
-      "bus",
-      "truck",
-      "motorcycle",
-      "van"
-    ],
-    "evaluated_element_revision": null,
-    "dataset_id": "7f5bf8d2-bf7a-4ecc-9d2f-b1071588f225",
-    "dataset_version_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "run_id": "bfb9a039-8a71-4ab5-9097-117d87572930",
-    "split_ref": "private_final",
-    "selected_frame_count": 91,
-    "selected_frame_indices": [
-      4,
-      8,
-      9,
-      12,
-      18,
-      22,
-      23,
-      26,
-      33,
-      34,
-      40,
-      49,
-      50,
-      62,
-      63,
-      68,
-      74,
-      76,
-      77,
-      89,
-      96,
-      98,
-      99,
-      100,
-      103,
-      105,
-      106,
-      113,
-      125,
-      128,
-      130,
-      137,
-      141,
-      144,
-      146,
-      148,
-      149,
-      154,
-      155,
-      156,
-      161,
-      164,
-      167,
-      169,
-      177,
-      184,
-      187,
-      193,
-      194,
-      201,
-      202,
-      204,
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-      228,
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-      246,
-      249,
-      252,
-      255,
-      256,
-      259,
-      261,
-      264,
-      266,
-      277,
-      288,
-      290,
-      315,
-      318,
-      319,
-      325,
-      329,
-      340,
-      342,
-      344,
-      347,
-      356,
-      365,
-      374,
-      380,
-      387,
-      389,
-      393,
-      403
-    ],
-    "evaluated_version_ids": [
-      "bfb9a039-8a71-4ab5-9097-117d87572930"
-    ],
-    "skipped_missing_pairs": 0
-  }
-}

main.py

@@ -146,16 +146,32 @@ def _to_detection(box: Any, class_names: list[str]) -> dict[str, Any]:
     return detection
 
 
-def _require_bgr_frame(image: Any) -> Any:
-    # Same frame the turbovision validator hands predict_batch: cv2.imdecode(...,
-    # IMREAD_COLOR) -> BGR uint8 HWC with 3 channels. Anything else is a harness
-    # bug and must fail loudly rather than be coerced.
-    if not isinstance(image, np.ndarray):
-        raise TypeError(f"frame must be a numpy.ndarray (BGR uint8 HWC), got {type(image).__name__}")
-    if image.dtype != np.uint8:
-        raise TypeError(f"frame dtype must be uint8, got {image.dtype}")
-    if image.ndim != 3 or image.shape[2] != 3:
-        raise TypeError(f"frame must be HWC with 3 channels (BGR), got shape {image.shape}")
+def _normalize_image_for_miner(image: Any) -> Any:
+    if image is None or hasattr(image, "shape"):
+        return image
+    if isinstance(image, (bytes, bytearray, memoryview)):
+        try:
+            buffer = np.frombuffer(bytes(image), dtype=np.uint8)
+            decoded = cv2.imdecode(buffer, cv2.IMREAD_COLOR)
+            if decoded is not None:
+                return decoded
+        except Exception:
+            return image
+    if hasattr(image, "convert") and callable(image.convert):
+        try:
+            rgb = image.convert("RGB")
+            array = np.array(rgb)
+            if getattr(array, "ndim", 0) == 3 and array.shape[-1] == 3:
+                return cv2.cvtColor(array, cv2.COLOR_RGB2BGR)
+            return array
+        except Exception:
+            return image
+    try:
+        array = np.asarray(image)
+        if getattr(array, "shape", None):
+            return array
+    except Exception:
+        return image
     return image
 
 
@@ -171,14 +187,34 @@ def load_model(onnx_path: str | None = None, data_dir: str | None = None):
     }
 
 
-# Validator chute: predict_batch(batch_images=..., offset=0, n_keypoints=32)
-# (turbovision chute_template :376-380; scorevision/utils/challenges.py:412).
-VALIDATOR_N_KEYPOINTS = 32
+def _candidate_keypoint_counts(miner: Any) -> list[int]:
+    counts: list[int] = [0]
+    for attr in ("n_keypoints", "num_keypoints", "keypoint_count", "num_joints"):
+        value = getattr(miner, attr, None)
+        if isinstance(value, int) and value > 0:
+            counts.append(value)
+    counts.append(32)
+
+    seen: set[int] = set()
+    ordered: list[int] = []
+    for count in counts:
+        if count in seen:
+            continue
+        seen.add(count)
+        ordered.append(count)
+    return ordered
 
 
-def _predict_frame(miner: Any, image: Any) -> list[Any]:
-    frame = _require_bgr_frame(image)
-    return miner.predict_batch(batch_images=[frame], offset=0, n_keypoints=VALIDATOR_N_KEYPOINTS)
+def _predict_batch_with_fallbacks(miner: Any, image: Any) -> list[Any]:
+    normalized_image = _normalize_image_for_miner(image)
+    errors: list[str] = []
+    for n_keypoints in _candidate_keypoint_counts(miner):
+        try:
+            return miner.predict_batch([normalized_image], offset=0, n_keypoints=n_keypoints)
+        except Exception as exc:
+            errors.append(f"n_keypoints={n_keypoints} -> {exc}")
+            continue
+    raise RuntimeError("predict_batch failed for all keypoint candidates: " + " | ".join(errors))
 
 
 def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_dir: str | None = None):
@@ -190,7 +226,7 @@ def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_
     class_names = model.get("class_names")
     if not isinstance(class_names, list):
         class_names = list(CLASS_NAMES)
-    results = _predict_frame(miner, image)
+    results = _predict_batch_with_fallbacks(miner, image)
     if not results:
         return [[]]
     frame_boxes = _extract_boxes(results[0])

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "miner-element-adapter"
 version = "0.1.0"
-requires-python = ">=3.12"
+requires-python = ">=3.9"
 
 dependencies = [
   "numpy>=1.23",