subnet_bridge: copy winning miner repo into library

README.md

@@ -1,31 +1,24 @@
 ---
 tags:
 - element_type:detect
-- model:ultralytics-yolo
+- model:onnxruntime
 - subnet:winner
 - object:person
+
 manako:
   source: winner_fetch
   manifest_element_name: manak0/Detect-Person
-  winner_repo_id: smile0123/nmodel262
-  winner_revision: df364ffd90211f8688b9b1fa219ace083dcd4c05
-  score:
-    size_mb: 10.710215
-    performance_map50: 0.7599817092537609
-    E: 0.07095858572902233
-  last_benchmark:
-    type: synthetic_fixed
-    ran_at: '2026-09-22T09:44:06.248024Z'
-    result_path: benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json
+  winner_repo_id: Lucky221115/Person-ML3
+  winner_revision: e4bfc487e3460e23b6ccea155de9f35b8a7f3fd0
 ---
 
 # Detect-Person-winner
 
 Published winning miner converted into a library element.
 
-- Source winner repo: `smile0123/nmodel262`
-- Source revision: `df364ffd90211f8688b9b1fa219ace083dcd4c05`
+- Source winner repo: `Lucky221115/Person-ML3`
+- Source revision: `e4bfc487e3460e23b6ccea155de9f35b8a7f3fd0`
 - Manifest element: `manak0/Detect-Person`
 - Element type: `Detect`
 - Objects: person
-- Runtime model type: `ultralytics-yolo`
+- Runtime model type: `onnxruntime`

benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json

@@ -1,80 +0,0 @@
-{
-  "meta": {
-    "element_id": "manak0/Detect-Person-winner",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "benchmark_type": "synthetic_fixed",
-    "saved_at": "2026-09-22T09:44:06.248024Z",
-    "result_path": "benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json",
-    "split_ref": "private_leaderboard"
-  },
-  "results": {
-    "overall_iou": 0.7417574323431301,
-    "per_class_iou": {
-      "person": 0.7417574323431301
-    },
-    "map_50": 0.7599817092537609,
-    "map_50_90": 0.5303797022812917,
-    "precision": 0.8327219369038885,
-    "recall": 0.7948179271708683,
-    "per_class_map_50": {
-      "person": 0.7599817092537609
-    },
-    "per_class_map_50_90": {
-      "person": 0.5303797022812917
-    },
-    "per_class_precision": {
-      "person": 0.8327219369038885
-    },
-    "per_class_recall": {
-      "person": 0.7948179271708683
-    },
-    "matched_images": 30,
-    "gt_count": 1428,
-    "pred_count": 1363,
-    "classes": [
-      "person"
-    ],
-    "evaluated_element_revision": null,
-    "dataset_id": "bdf4d5b4-7dec-4b72-8747-ab005ca92b3b",
-    "dataset_version_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "split_ref": "private_leaderboard",
-    "selected_frame_count": 30,
-    "selected_frame_indices": [
-      1,
-      2,
-      3,
-      4,
-      5,
-      6,
-      7,
-      8,
-      9,
-      10,
-      11,
-      12,
-      13,
-      14,
-      15,
-      16,
-      17,
-      18,
-      19,
-      20,
-      21,
-      22,
-      23,
-      24,
-      25,
-      26,
-      27,
-      28,
-      29,
-      30
-    ],
-    "evaluated_version_ids": [
-      "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92"
-    ],
-    "skipped_missing_pairs": 0
-  }
-}

benchmark/synthetic/index.json

@@ -1,16 +0,0 @@
-[
-  {
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "saved_at": "2026-09-22T09:44:06.248024Z",
-    "result_path": "benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json",
-    "overall_iou": 0.7417574323431301,
-    "map_50": 0.7599817092537609,
-    "map_50_90": 0.5303797022812917,
-    "precision": 0.8327219369038885,
-    "recall": 0.7948179271708683,
-    "matched_images": 30,
-    "gt_count": 1428,
-    "pred_count": 1363,
-    "split_ref": "private_leaderboard"
-  }
-]

benchmark/synthetic/latest.json

@@ -1,80 +0,0 @@
-{
-  "meta": {
-    "element_id": "manak0/Detect-Person-winner",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "benchmark_type": "synthetic_fixed",
-    "saved_at": "2026-09-22T09:44:06.248024Z",
-    "result_path": "benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json",
-    "split_ref": "private_leaderboard"
-  },
-  "results": {
-    "overall_iou": 0.7417574323431301,
-    "per_class_iou": {
-      "person": 0.7417574323431301
-    },
-    "map_50": 0.7599817092537609,
-    "map_50_90": 0.5303797022812917,
-    "precision": 0.8327219369038885,
-    "recall": 0.7948179271708683,
-    "per_class_map_50": {
-      "person": 0.7599817092537609
-    },
-    "per_class_map_50_90": {
-      "person": 0.5303797022812917
-    },
-    "per_class_precision": {
-      "person": 0.8327219369038885
-    },
-    "per_class_recall": {
-      "person": 0.7948179271708683
-    },
-    "matched_images": 30,
-    "gt_count": 1428,
-    "pred_count": 1363,
-    "classes": [
-      "person"
-    ],
-    "evaluated_element_revision": null,
-    "dataset_id": "bdf4d5b4-7dec-4b72-8747-ab005ca92b3b",
-    "dataset_version_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "split_ref": "private_leaderboard",
-    "selected_frame_count": 30,
-    "selected_frame_indices": [
-      1,
-      2,
-      3,
-      4,
-      5,
-      6,
-      7,
-      8,
-      9,
-      10,
-      11,
-      12,
-      13,
-      14,
-      15,
-      16,
-      17,
-      18,
-      19,
-      20,
-      21,
-      22,
-      23,
-      24,
-      25,
-      26,
-      27,
-      28,
-      29,
-      30
-    ],
-    "evaluated_version_ids": [
-      "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92"
-    ],
-    "skipped_missing_pairs": 0
-  }
-}

main.py

@@ -28,7 +28,7 @@ Miner = _load_local_miner_class()
 
 
 CLASS_NAMES = ['person']
-MODEL_TYPE = 'ultralytics-yolo'
+MODEL_TYPE = 'onnxruntime'
 
 
 def _to_dict(value: Any) -> dict[str, Any]:

miner.py

@@ -1,5 +1,9 @@
+import ast
+import json
+import math
 import os
 from pathlib import Path
+from typing import Any
 
 import cv2
 import numpy as np
@@ -7,31 +11,28 @@ import onnxruntime as ort
 from numpy import ndarray
 from pydantic import BaseModel
 
+API_VERSION = "c2-strict-v1"
+STRICT_INFERENCE_ENV = "SV_STRICT_INFERENCE"
+OUTPUT_ROWS = 300
+OUTPUT_COLUMNS = 6
 
-# =============================================================================
-# Person miner (manak0/Detect-Person) — port of the proven carwash001 miner,
-# same structure as VehicleDetection/miner/my_miner.
-#
-# Model: yolo26n end2end ONNX @704 (person_0904_704 / powerful_selected soup),
-# single element-canonical class: 0=person.
-#
-# Carried over (all validated on the carwash/vehicle elements):
-#   * robust model loading, FP16 auto-detect, metadata class order, ORT thread
-#     pinning, raw-YOLO decode fallback, NO CLAHE (train/test match)
-#   * vectorized hard NMS + cluster-max boost, per-class NMS IoU / min-area,
-#     pre_nms_topk, cross-class dedup (a no-op guard with one class),
-#     square-domain hook
-#   * soft-NMS with post-decay re-filter (use_soft_nms), rescue bonus — both
-#     SWEEPABLE via tune_miner.py; which setting wins is element-specific
-#     (carwash: soft on / cluster on / rescue off; vehicle: the opposite).
-#
-# The vehicle miner's dual-label bus/truck lever is intentionally absent:
-# person has no sibling class to hedge against.
-#
-# Run tune_miner.py after ANY retrain/re-export and paste its printout below.
-# A failing conformity tuple (latency/format) removes the miner from scoring
-# entirely — bench latency with bench_latency.py (2 pinned CPUs) too.
-# =============================================================================
+ELEMENT_CONFIG: dict[str, Any] = {
+    "element_id": "person",
+    "classes": ["person"],
+    "imgsz": 704,
+    "conf": 0.3,
+    "max_det": 300,
+    "min_side": 3,
+    "max_aspect": 10,
+    "max_area_frac": 0.95,
+    "scale_w": 1,
+    "scale_h": 1,
+    "conf_steps": ((30, 0.39), (50, 0.46), (None, 0.58)),
+}
+
+
+class InferenceInvariantError(RuntimeError):
+    pass
 
 
 class BoundingBox(BaseModel):
@@ -49,788 +50,220 @@ class TVFrameResult(BaseModel):
     keypoints: list[tuple[int, int]]
 
 
-class Miner:
-    def __init__(self, path_hf_repo: Path) -> None:
-        model_path = self._resolve_model_path(path_hf_repo)
-        # Canonical class order every downstream consumer (validator,
-        # BoundingBox.cls_id) sees — MUST equal element.objects order:
-        self.class_names = ["person"]
-        # Fallback model-emit order (used only when ONNX metadata is missing).
-        self._model_class_order = ["person"]
-        print("ORT version:", ort.__version__)
-
-        try:
-            ort.preload_dlls()
-            print("onnxruntime.preload_dlls() success")
-        except Exception as e:
-            print(f"preload_dlls failed: {e}")
+def _conf_for(n_raw: int) -> float:
+    for limit, conf in ELEMENT_CONFIG.get("conf_steps", ()):
+        if limit is None or n_raw < limit:
+            return float(conf)
+    return float(ELEMENT_CONFIG["conf"])
 
-        print("ORT available providers BEFORE session:", ort.get_available_providers())
 
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-        # Pin threads for the CPU latency gate.
-        sess_options.intra_op_num_threads = 2
-        sess_options.inter_op_num_threads = 1
-        sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+class Miner:
+    api_version = API_VERSION
+    strict_inference = False
 
-        # MINER_FORCE_CPU=1 skips the CUDA attempt — used by bench_latency.py to
-        # measure the conformity-gate path (this box has onnxruntime-gpu too, so
-        # an unforced "CPU" benchmark silently runs on the GPU).
-        force_cpu = os.environ.get("MINER_FORCE_CPU", "") == "1"
+    def __init__(self, path_hf_repo: Path) -> None:
+        cfg = ELEMENT_CONFIG
+        self.config = cfg
+        self.class_names = list(cfg["classes"])
+        self.conf_threshold = float(cfg["conf"])
+        self.max_det = int(cfg["max_det"])
+        self.min_side = float(cfg.get("min_side", 3.0))
+        self.max_aspect_ratio = float(cfg.get("max_aspect", 10.0))
+        self.max_area_frac = float(cfg.get("max_area_frac", 0.95))
+        self.conf_steps = tuple(tuple(s) for s in cfg.get("conf_steps", ()))
+        self.scale_w = float(cfg.get("scale_w", 1.0))
+        self.scale_h = float(cfg.get("scale_h", 1.0))
+        raw = os.environ.get(STRICT_INFERENCE_ENV, "")
+        if raw not in ("", "0", "1"):
+            raise ValueError(f"{STRICT_INFERENCE_ENV} must be '1' or unset/'0', got {raw!r}")
+        self.strict_inference = raw == "1"
+        self.inference_failures = 0
+
+        cv2.setNumThreads(1)
+        options = ort.SessionOptions()
+        options.intra_op_num_threads = 2
+        options.inter_op_num_threads = 1
+        options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+        options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        model_path = Path(path_hf_repo) / "weights.onnx"
         try:
-            if force_cpu:
-                raise RuntimeError("MINER_FORCE_CPU=1")
             self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
+                str(model_path), sess_options=options,
                 providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
             )
-            print("Created ORT session with preferred CUDA provider list")
-        except Exception as e:
-            print(f"CUDA session creation skipped/failed, using CPU: {e}")
+        except Exception as exc:
+            print(f"CUDA session failed, falling back to CPU: {exc}")
             self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CPUExecutionProvider"],
+                str(model_path), sess_options=options, providers=["CPUExecutionProvider"]
             )
-        print("ORT session providers:", self.session.get_providers())
-
-        # cls_remap[i] = self.class_names.index(model_class_order[i]); order comes
-        # from ONNX metadata when present, else the static fallback.
-        model_class_order = self._read_model_class_order()
-        if model_class_order is None:
-            model_class_order = list(self._model_class_order)
-            print(f"cls order: no usable ONNX metadata, FALLBACK {model_class_order}")
-        else:
-            print(f"cls order: from ONNX metadata {model_class_order}")
-        self.cls_remap = np.array(
-            [self.class_names.index(n) for n in model_class_order], dtype=np.int32
-        )
-
-        for inp in self.session.get_inputs():
-            print("INPUT:", inp.name, inp.shape, inp.type)
-        for out in self.session.get_outputs():
-            print("OUTPUT:", out.name, out.shape, out.type)
 
         self.input_name = self.session.get_inputs()[0].name
         self.output_names = [o.name for o in self.session.get_outputs()]
-        self.input_shape = self.session.get_inputs()[0].shape
-
-        # Match the ONNX input dtype (FP16 export needs float16 input).
-        input_type = self.session.get_inputs()[0].type
-        self.np_dtype = np.float16 if "float16" in input_type else np.float32
-        print(f"ONNX input dtype: {input_type} -> numpy {self.np_dtype}")
-
-        # The miner MUST run at the ONNX's baked input size (704). Dropping to
-        # 640 is a latency lever but an EXPORT decision (re-export + re-eval),
-        # not a miner-side change.
-        self.input_height = self._safe_dim(self.input_shape[2], default=704)
-        self.input_width = self._safe_dim(self.input_shape[3], default=704)
-
-        # ── Post-processing config (tune on person1-powerful/val) ─────────────
-        # Per-class NMS IoU ([person]).
-        self._iou_thres_array = np.array([0.45], dtype=np.float32)
-        self.cross_iou_thresh = 0.9   # near-duplicate guard (no-op across classes
-                                      # with one class; catches post-NMS dupes)
-        self.max_det = 300            # person frames can be busy
-        self.pre_nms_topk = 1000      # cap candidates before NMS (crowded-frame speed guard)
-        self.use_tta = False          # 2nd flipped pass; doubles latency -> off for CPU gate
-
-        # Single-view cluster boost: raise each survivor's conf to its
-        # IoU-cluster max. OFF per the 2026-09-04 person sweep (carwash: on,
-        # vehicle: off -- element-specific, re-sweep on new weights).
-        self.use_cluster_boost = False
-
-        # Soft-NMS: Gaussian decay of overlapping scores instead of hard
-        # deletion, then RE-FILTER decayed scores against the confidence
-        # threshold (keeps decayed junk away from the FP pillar).
-        # OFF per the 2026-09-04 person sweep (off 0.5670 vs on 0.5662) --
-        # element- and weights-specific, re-sweep after every retrain.
-        self.use_soft_nms = True
-        self.soft_nms_sigma = 0.4
-
-        # Confidence threshold ([person]). `_conf_thres_array` is the SQUARE /
-        # validator-eval set (every scored frame is 1024x1024 squished, so this
-        # is the one that matters); `_extra` covers non-square inputs (warmup /
-        # any 16:9 frame). Tuned 2026-09-04 (tune_miner.py, 1853-img
-        # person1-powerful/val, adaptive-IoU matcher): comp 0.5385 -> 0.5670.
-        # NOTE: the val is dense (29 GT/frame avg) and recall is the binding
-        # constraint. A low-edge extension sweep found a plateau below the
-        # floor (0.05-0.09 -> comp 0.5713, +0.004) but on REAL challenge
-        # frames the 0.05-0.12 band adds ~25 boxes/frame -- too FP-risky to
-        # ship on dense-val evidence alone. Decide with test_miner.py + SAM3
-        # GT on real frames; retune after every retrain (especially once the
-        # sparse regenerated data lands).
-        self._conf_thres_array = np.array([0.25], dtype=np.float32)
-        self._extra_conf_thres_array = np.array([0.25], dtype=np.float32)
-
-        # Rescue bonus: if NO box passes the threshold, admit the top-1 when
-        # score >= (threshold - bonus). ON per the 2026-09-04 sweep.
-        self._bonus_array = np.full(1, 0.03, dtype=np.float32)
-
-        # Min box area (px^2), loose: distant people are tiny in 1024x1024
-        # frames and tiny-GT matches only need IoU 0.30 under the adaptive
-        # matcher — do not silently drop them.
-        self._min_box_area_array = np.array([12.0], dtype=np.float32)
-        self.min_side = 3
-        self.max_aspect_ratio = 12.0
-        self.max_area_frac = 0.95
-
-        # Density-adaptive confidence ("CONF_STEPS"): after NMS, count the
-        # frame's survivors and re-gate them at a threshold that rises with the
-        # crowd. Rationale: the validator charges false positives PER FRAME
-        # (false_positive = max(0, 1 - FP_per_image / 10)), so a busy frame --
-        # which generates the most FP -- can in principle afford a stricter bar.
-        #
-        # Entries are (upper_bound, threshold), first bound the count falls
-        # under wins, None = catch-all; if none match, the last threshold is
-        # used. () disables the lever. The config-driven miner shipped
-        # ((30, 0.5), (50, 0.55), (None, 0.6)).
-        #
-        # SET to the "gentle" schedule (chosen 2026-09-10). Measured on THESE
-        # weights (md5 4ca5622f = person4_cctv_yolo26n_704_0909) against the
-        # real turbovision pillars, delta composite vs the lever OFF:
-        #
-        #   schedule                          p4/test  p-11/197  merged/test
-        #   ((30,.45),(60,.50),(None,.55))    -0.0001   -0.0011     -0.0006   <- set
-        #   ((30,.5),(50,.55),(None,.6))      -0.0119   -0.0081     -0.0161
-        #   ((40,.47),(None,.50))             -0.0044   -0.0026     -0.0063
-        #   inverted (loosen when crowded)    -0.0261   -0.0074     -0.0311
-        #
-        # This schedule is the cheapest of the four: it costs ~0.001 composite,
-        # which is within run-to-run noise, where the ported one costs 10-16x
-        # that. It is cheap because its first step (0.45) EQUALS
-        # `_conf_thres_array`, so frames under 30 boxes are untouched -- only
-        # the 0.50 and 0.55 tiers ever bite:
-        #
-        #   set          frames  @0.45  @0.50  @0.55  frames changed  boxes cut
-        #   p4/test        1702   1573    120      9      117 ( 6.9%)     1.68%
-        #   person-11       197    153     37      7       34 (17.3%)     2.55%
-        #   merged/test    1605   1591     14      0       14 ( 0.9%)     0.39%
-        #
-        # COUPLING: if `_conf_thres_array` is ever lowered below 0.45, the first
-        # step stops being a no-op and starts gating every sparse frame -- re-run
-        # sweep_conf_steps.py before changing either value.
-        #
-        # Verified over all 3504 frames above: 3310 land in the 0.45 tier and
-        # NONE loses a box. Exactly one holds a sub-0.45 box (admitted by the
-        # rescue bonus in `_conf_filter_mask`, floor - 0.03 = 0.42); it survives
-        # only because `_apply_density_gate` keeps the top-1 rather than
-        # emptying the frame. Drop that guarantee and this schedule silently
-        # zeroes that frame's recall.
-        #
-        # No schedule tested beat OFF; this one is a near-tie. Inverting the
-        # direction is worse than the ported schedule, which says the premise --
-        # that crowded frames carry the excess FP -- does not hold for this
-        # model: its FP are spread across sparse frames too.
-        #
-        # SWEEPABLE: sweep_conf_steps.py (same dir) re-measures this table
-        # against the real turbovision pillars -- re-run it after any retrain.
-        # NOT tune_miner.py: that is a scoring library
-        # (evaluate_validator/adaptive_thr), it does not sweep levers.
-        self.conf_steps: tuple[tuple[int | None, float], ...] = (
-            (50, 0.25), (90, 0.33), (None, 0.38),
+        shape = self.session.get_inputs()[0].shape
+        if len(shape) != 4 or shape[1] != 3:
+            raise ValueError(f"model input must be rank-4 NCHW with 3 channels, got {shape!r}")
+        if not all(isinstance(v, int) and v > 0 for v in shape[2:]):
+            raise ValueError(f"e2e export must have static spatial dims, got {shape[2:]!r}")
+        if (shape[2], shape[3]) != (cfg["imgsz"], cfg["imgsz"]):
+            raise ValueError(f"model input {shape[2]}x{shape[3]} != config imgsz {cfg['imgsz']}")
+        self.input_height = self.input_width = int(cfg["imgsz"])
+        out_shape = self.session.get_outputs()[0].shape
+        if list(out_shape) != [1, OUTPUT_ROWS, OUTPUT_COLUMNS]:
+            raise ValueError(f"e2e output must be [1, {OUTPUT_ROWS}, {OUTPUT_COLUMNS}], got {out_shape!r}")
+        self.model_names = self._read_model_names()
+        self._warmup(3)
+        print(
+            f"api_version={self.api_version} element={cfg['element_id']} imgsz={cfg['imgsz']} "
+            f"conf={self.conf_threshold} steps={self.conf_steps} "
+            f"providers={self.session.get_providers()}"
         )
 
-
-        # Square-domain de-squish: widen a square (validator) frame by this
-        # fraction before letterboxing, then drop pad-center boxes and un-pad.
-        # 0.0 = off — the model was TRAINED on squished 1024 frames, so the
-        # domains already match; enabling changes aspect -> validate first.
-        self.square_pad_frac = 0.0
-
-        # uint8 -> float32/255 as a 256-entry table lookup: same numbers as a
-        # multiply, measurably cheaper and steadier per frame. Read-only after
-        # construction, so it stays safe to share across concurrent calls.
-        self._scale_lut = (np.arange(256, dtype=np.float32) / 255.0).reshape(1, 256)
-
-        self._avg_iou = float(np.mean(self._iou_thres_array))
-        print(f"ONNX model loaded from: {model_path}")
-        print(f"ONNX input: name={self.input_name}, shape={self.input_shape}")
-        self._warmup()
-
-    # ── Setup helpers ────────────────────────────────────────────────────────
-
-    def _warmup(self, iters: int = 3) -> None:
+    def _read_model_names(self) -> tuple[str, ...]:
         try:
-            dummy = np.zeros((1024, 1024, 3), dtype=np.uint8)  # validator domain
-            for _ in range(max(1, iters)):
-                self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0)
-            print(f"warmup: {iters} dummy predict_batch call(s) done")
-        except Exception as e:
-            print(f"warmup skipped: {e}")
-
-    def __repr__(self) -> str:
-        return f"PersonMiner(classes={len(self.class_names)}, providers={self.session.get_providers()})"
-
-    @staticmethod
-    def _safe_dim(value, default: int) -> int:
-        return value if isinstance(value, int) and value > 0 else default
+            raw = self.session.get_modelmeta().custom_metadata_map["names"]
+            parsed = ast.literal_eval(raw)
+            if not isinstance(parsed, dict):
+                raise TypeError("names metadata is not a dict")
+            keys = sorted(parsed)
+            if keys != list(range(len(keys))):
+                raise ValueError(f"names metadata keys not consecutive from zero: {keys[:8]}")
+            return tuple(str(parsed[i]) for i in keys)
+        except Exception as exc:
+            raise ValueError(f"e2e ONNX requires valid names metadata: {exc}") from exc
 
     @staticmethod
-    def _resolve_model_path(repo: Path) -> Path:
-        """Prefer weights.onnx, then weights_int8.onnx, then any .onnx."""
-        for name in ("weights.onnx", "weights_int8.onnx"):
-            p = repo / name
-            if p.exists():
-                if name != "weights.onnx":
-                    print(f"model: weights.onnx not found, using {name}")
-                return p
-        candidates = sorted(repo.glob("*.onnx"))
-        if candidates:
-            print(f"model: using {candidates[0].name}")
-            return candidates[0]
-        return repo / "weights.onnx"  # let session creation raise the error
-
-    def _read_model_class_order(self) -> list[str] | None:
-        """Read class order from Ultralytics ONNX `names` metadata, or None."""
-        try:
-            import ast
-            meta = self.session.get_modelmeta().custom_metadata_map
-            names = ast.literal_eval(meta["names"])
-            order = ([str(names[i]) for i in sorted(names)] if isinstance(names, dict)
-                     else [str(n) for n in names])
-        except Exception as e:
-            print(f"cls order: could not read ONNX names metadata ({e})")
-            return None
-        if sorted(order) != sorted(self.class_names):
-            print(f"cls order: ONNX names {order} != expected {self.class_names}; ignoring")
-            return None
-        return order
-
-    # ── Preprocessing ────────────────────────────────────────────────────────
-
-    def _letterbox(self, image: ndarray, new_shape: tuple[int, int],
-                   color=(114, 114, 114)) -> tuple[ndarray, float, tuple[float, float]]:
-        h, w = image.shape[:2]
-        new_w, new_h = new_shape
-        ratio = min(new_w / w, new_h / h)
-        rw, rh = int(round(w * ratio)), int(round(h * ratio))
-        if (rw, rh) != (w, h):
-            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            image = cv2.resize(image, (rw, rh), interpolation=interp)
-        dw = (new_w - rw) / 2.0
-        dh = (new_h - rh) / 2.0
-        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-        if left or right or top or bottom:
-            image = cv2.copyMakeBorder(image, top, bottom, left, right,
-                                       cv2.BORDER_CONSTANT, value=color)
-        # A square validator frame letterboxes to zero padding -- copying the
-        # image just to add no border is pure latency, so skip it.
-        return image, ratio, (dw, dh)
-
-    def _to_blob(self, img: ndarray) -> ndarray:
-        """HWC-BGR uint8 -> NCHW-RGB float32 /255.
-
-        cv2.split + cv2.LUT write the de-interleaved, scaled planes straight
-        into the output blob: two C passes and one allocation, where the old
-        `cvtColor -> astype -> /255 -> transpose -> ascontiguousarray` chain
-        reached the same numbers through four full-size temporaries and a
-        strided 6 MB copy.
-
-        The blob is allocated per call ON PURPOSE. Hoisting it (and the split
-        planes) into reused instance buffers measured only ~0.07 ms/frame
-        better and makes the miner unsafe to call from two threads at once --
-        chute_config.yml sets `concurrency: 4`, and a shared buffer there
-        silently returns one request's boxes for another request's frame.
-        """
-        h, w = img.shape[:2]
-        blob = np.empty((1, 3, h, w), dtype=np.float32)
-        b, g, r = cv2.split(img)
-        lut = self._scale_lut
-        cv2.LUT(r, lut, dst=blob[0, 0])
-        cv2.LUT(g, lut, dst=blob[0, 1])
-        cv2.LUT(b, lut, dst=blob[0, 2])
-        if self.np_dtype != np.float32:             # FP16 export
-            return blob.astype(self.np_dtype)
-        return blob
-
-    def _preprocess(self, image: ndarray) -> tuple[np.ndarray, dict]:
-        """Letterbox to the ONNX input size; NO CLAHE/denoise/sharpen (any
-        enhancement the model wasn't trained on is a train/test mismatch that
-        HURTS accuracy and also risks the CPU latency gate).
-
-        Square-domain de-squish: when the frame is square (the validator's
-        1024x1024 squished eval image) and square_pad_frac > 0, widen it with
-        gray bars first -- objects come out horizontally compressed in those
-        frames, and mild widening partly restores their aspect.
-        """
-        orig_h, orig_w = image.shape[:2]
-        extra_left = extra_right = 0
-        if self.square_pad_frac > 0.0 and orig_w == orig_h:
-            target_w = int(orig_w * (1.0 + self.square_pad_frac))
-            if target_w > orig_w:
-                total = target_w - orig_w
-                extra_left = total // 2
-                extra_right = total - extra_left
-                image = cv2.copyMakeBorder(image, 0, 0, extra_left, extra_right,
-                                           cv2.BORDER_CONSTANT, value=(114, 114, 114))
-
-        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
-        img = self._to_blob(img)
-        return img, {
-            "ratio": ratio, "pad": pad, "orig_size": (orig_w, orig_h),
-            "extra_left": extra_left, "extra_right": extra_right,
-        }
-
-    # ── Vectorized box ops ───────────────────────────────────────────────────
-
-    @staticmethod
-    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
-        w, h = image_size
-        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
-        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
-        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
-        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
-        return boxes
-
-    @staticmethod
-    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
-        out = np.empty_like(boxes)
-        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-        return out
-
-    @staticmethod
-    def _hard_nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> np.ndarray:
-        """Vectorized greedy NMS: areas precomputed once, each step is a single
-        numpy IoU vector -- no inner Python loop."""
-        n = len(boxes)
-        if n == 0:
-            return np.array([], dtype=np.intp)
-        x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
-        areas = np.maximum(0.0, x2 - x1) * np.maximum(0.0, y2 - y1)
-        order = np.argsort(-scores)
-        keep = []
-        while order.size > 0:
-            i = int(order[0])
-            keep.append(i)
-            if order.size == 1:
-                break
-            rest = order[1:]
-            xx1 = np.maximum(x1[i], x1[rest])
-            yy1 = np.maximum(y1[i], y1[rest])
-            xx2 = np.minimum(x2[i], x2[rest])
-            yy2 = np.minimum(y2[i], y2[rest])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            iou = inter / (areas[i] + areas[rest] - inter + 1e-7)
-            order = rest[iou <= iou_thresh]
-        return np.array(keep, dtype=np.intp)
-
-    @staticmethod
-    def _soft_nms(boxes: np.ndarray, scores: np.ndarray, sigma: float
-                  ) -> tuple[np.ndarray, np.ndarray]:
-        """Gaussian soft-NMS: decay overlapping scores instead of deleting.
-        Returns (original indices in decay order, decayed scores).
-
-        Pairwise IoU does not change as scores decay, so the IoU -> decay
-        matrix is built ONCE and each step is an argmax plus one multiply.
-        Same arithmetic as recomputing a geometry row per step, at a fraction
-        of the numpy call overhead -- which, at a few dozen boxes per frame, is
-        what this loop actually costs. Verified box-for-box identical to the
-        per-step version on real frames; the only behaviour that can differ is
-        which of two byte-identical duplicate boxes with equal scores is listed
-        first."""
-        n = len(boxes)
-        if n == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        b = boxes.astype(np.float32, copy=False)
-        x1, y1, x2, y2 = b[:, 0], b[:, 1], b[:, 2], b[:, 3]
-        areas = np.maximum(0.0, x2 - x1) * np.maximum(0.0, y2 - y1)
-        inter = (np.maximum(0.0, np.minimum(x2[:, None], x2[None, :])
-                            - np.maximum(x1[:, None], x1[None, :])) *
-                 np.maximum(0.0, np.minimum(y2[:, None], y2[None, :])
-                            - np.maximum(y1[:, None], y1[None, :])))
-        iou = inter / (areas[:, None] + areas[None, :] - inter + 1e-7)
-        decay = np.exp(-(iou ** 2) / sigma)
-
-        # Picked boxes are parked at -inf instead of being deleted from the
-        # working arrays, so each step is just an argmax and one in-place
-        # multiply over a fixed-size vector -- no per-step reallocation.
-        # `where=alive` keeps the multiply off the parked entries: a small
-        # enough soft_nms_sigma (a swept knob) underflows exp() to 0.0 in
-        # float32, and -inf * 0.0 would be NaN -- which argmax then picks,
-        # re-selecting a dead box.
-        sc = scores.astype(np.float32, copy=True)
-        alive = np.ones(n, dtype=bool)
-        order = np.empty(n, dtype=np.intp)
-        out_sc = np.empty(n, dtype=np.float32)
-        for i in range(n):
-            k = int(np.argmax(sc))
-            order[i] = k
-            out_sc[i] = sc[k]
-            if i + 1 >= n:
-                break
-            sc[k] = -np.inf
-            alive[k] = False
-            np.multiply(sc, decay[k], out=sc, where=alive)
-        return order, out_sc
-
-    def _per_class_soft_nms(self, boxes: np.ndarray, scores: np.ndarray,
-                            cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-        """Soft-NMS per class, then re-filter decayed scores against the
-        per-class confidence thresholds so no decayed box reaches the FP pillar
-        below its class bar."""
-        keep_idx: list[np.ndarray] = []
-        keep_sc: list[np.ndarray] = []
-        for c in np.unique(cls_ids):
-            idx = np.where(cls_ids == c)[0]
-            order, dec = self._soft_nms(boxes[idx], scores[idx], self.soft_nms_sigma)
-            thr = float(self._conf_thres_array[c])
-            surv = dec >= thr
-            keep_idx.append(idx[order[surv]])
-            keep_sc.append(dec[surv])
-        if not keep_idx:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        return (np.concatenate(keep_idx).astype(np.intp, copy=False),
-                np.concatenate(keep_sc).astype(np.float32, copy=False))
-
-    def _per_class_hard_nms(self, boxes: np.ndarray, scores: np.ndarray,
-                            cls_ids: np.ndarray) -> np.ndarray:
-        """Per-class NMS using the per-class IoU thresholds."""
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp)
-        all_keep: list[int] = []
-        for c in np.unique(cls_ids):
-            idx = np.where(cls_ids == c)[0]
-            cls_iou = float(self._iou_thres_array[c])
-            keep = self._hard_nms(boxes[idx], scores[idx], cls_iou)
-            all_keep.extend(idx[keep].tolist())
-        all_keep.sort()
-        return np.array(all_keep, dtype=np.intp)
-
-    @staticmethod
-    def _max_score_per_cluster(post_boxes: np.ndarray, post_cls: np.ndarray,
-                               full_boxes: np.ndarray, full_scores: np.ndarray,
-                               full_cls: np.ndarray, iou_thresh: float) -> np.ndarray:
-        """Each survivor's confidence -> max score in its SAME-CLASS IoU cluster.
-        Vectorized single (n_post x n_full) IoU matrix."""
-        n = len(post_boxes)
-        if n == 0:
-            return np.empty(0, dtype=np.float32)
-        m = len(full_boxes)
-        if m == 0:
-            return np.zeros(n, dtype=np.float32)
-        pa = (np.maximum(0.0, post_boxes[:, 2] - post_boxes[:, 0]) *
-              np.maximum(0.0, post_boxes[:, 3] - post_boxes[:, 1]))
-        fa = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
-              np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
-        xx1 = np.maximum(post_boxes[:, 0][:, None], full_boxes[:, 0][None, :])
-        yy1 = np.maximum(post_boxes[:, 1][:, None], full_boxes[:, 1][None, :])
-        xx2 = np.minimum(post_boxes[:, 2][:, None], full_boxes[:, 2][None, :])
-        yy2 = np.minimum(post_boxes[:, 3][:, None], full_boxes[:, 3][None, :])
-        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-        iou = inter / (pa[:, None] + fa[None, :] - inter + 1e-7)
-        mask = (iou >= iou_thresh) & (post_cls[:, None] == full_cls[None, :])
-        tiled = np.where(mask, full_scores[None, :], -np.inf)
-        out = tiled.max(axis=1)
-        out[~np.isfinite(out)] = 0.0
-        return out.astype(np.float32)
-
-    def _conf_filter_mask(self, scores: np.ndarray, cls_ids: np.ndarray,
-                          extra_left: int) -> np.ndarray:
-        """Per-class threshold (square vs non-square set) + per-class rescue."""
-        if len(scores) == 0:
-            return np.zeros(0, dtype=bool)
-        thr_arr = self._extra_conf_thres_array if extra_left > 0 else self._conf_thres_array
-        keep = scores >= thr_arr[cls_ids]
-        for c in np.unique(cls_ids):
-            b = float(self._bonus_array[c])
-            if b <= 0.0:
-                continue
-            cm = cls_ids == c
-            if keep[cm].any():
-                continue
-            idx = np.where(cm)[0]
-            top = int(idx[int(np.argmax(scores[idx]))])
-            if scores[top] >= float(self._conf_thres_array[c]) - b:
-                keep[top] = True
-        return keep
-
-    def _filter_sane_boxes(self, boxes: np.ndarray, scores: np.ndarray,
-                           cls_ids: np.ndarray, orig_size: tuple[int, int]
-                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Vectorized per-class min-area / max-area / min-side / aspect filter."""
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
-        bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0])
-        bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
-        area = bw * bh
-        ar = np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6))
-        keep = (
-            (bw >= self.min_side) & (bh >= self.min_side) &
-            (area >= self._min_box_area_array[cls_ids]) &
-            (area <= self.max_area_frac * image_area) &
-            (ar <= self.max_aspect_ratio)
+    def _letterbox(image: ndarray, new_shape: tuple[int, int], color=(114, 114, 114)):
+        height, width = image.shape[:2]
+        new_width, new_height = new_shape
+        ratio = min(new_width / width, new_height / height)
+        rw, rh = int(round(width * ratio)), int(round(height * ratio))
+        if (rw, rh) != (width, height):
+            image = cv2.resize(
+                image, (rw, rh),
+                interpolation=cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR,
+            )
+        dw, dh = (new_width - rw) / 2.0, (new_height - rh) / 2.0
+        padded = cv2.copyMakeBorder(
+            image,
+            int(round(dh - 0.1)), int(round(dh + 0.1)),
+            int(round(dw - 0.1)), int(round(dw + 0.1)),
+            cv2.BORDER_CONSTANT, value=color,
         )
-        return boxes[keep], scores[keep], cls_ids[keep]
-
-    def _cross_class_dedup_op(self, boxes: np.ndarray, scores: np.ndarray,
-                              cls_ids: np.ndarray, iou_thresh: float
-                              ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Suppress near-duplicate boxes at IoU>thresh regardless of class.
-        With a single class this only catches rare post-NMS duplicates."""
-        n = len(boxes)
-        if n <= 1:
-            return boxes, scores, cls_ids
-        boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
-        cls_ids = np.asarray(cls_ids, dtype=np.int32)
-        x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
-        areas = np.maximum(0.0, x2 - x1) * np.maximum(0.0, y2 - y1)
-        margins = scores - self._conf_thres_array[cls_ids]
-        order = np.lexsort((-areas, -margins))
-        # One (n x n) IoU matrix instead of an n-long numpy pass per survivor;
-        # `a_i` keeps the old per-row 1e-7 area floor so the numbers match.
-        inter = (np.maximum(0.0, np.minimum(x2[:, None], x2[None, :])
-                            - np.maximum(x1[:, None], x1[None, :])) *
-                 np.maximum(0.0, np.minimum(y2[:, None], y2[None, :])
-                            - np.maximum(y1[:, None], y1[None, :])))
-        a_i = np.maximum(1e-7, (x2 - x1) * (y2 - y1))
-        dup = (inter / (a_i[:, None] + areas[None, :] - inter + 1e-7)) > iou_thresh
-        np.fill_diagonal(dup, False)
-        if not dup.any():
-            # Nothing overlaps at this threshold (the common case for a lever
-            # meant to catch rare post-NMS dupes) -- skip the greedy scan, but
-            # keep the margin/area ordering it would have produced.
-            return boxes[order], scores[order], cls_ids[order]
-        suppressed = np.zeros(n, dtype=bool)
-        keep: list[int] = []
-        for i in order:
-            if suppressed[i]:
-                continue
-            keep.append(int(i))
-            suppressed |= dup[i]
-        keep_idx = np.array(keep, dtype=np.intp)
-        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
-
-    def _density_conf(self, n_objects: int) -> float | None:
-        """Confidence floor for a frame holding `n_objects` boxes.
-
-        Returns None when the lever is off, so callers can skip the gate
-        entirely rather than compare against a sentinel.
-        """
-        if not self.conf_steps:
-            return None
-        for limit, thr in self.conf_steps:
-            if limit is None or n_objects < limit:
-                return float(thr)
-        return float(self.conf_steps[-1][1])
-
-    def _apply_density_gate(self, boxes: np.ndarray, scores: np.ndarray,
-                            cls_ids: np.ndarray
-                            ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Re-gate one frame's survivors at a crowd-size-dependent threshold.
-
-        Runs once per FRAME on final scores (after NMS, max_det, dedup and any
-        cluster boost) -- not per view, so TTA counts the merged set once.
-
-        Never empties a frame that had boxes: the top-1 is always kept, which
-        mirrors the rescue-bonus guarantee in `_conf_filter_mask`. Wiping a
-        frame to zero would forfeit the recall pillar outright for at most one
-        frame's worth of FP.
-        """
-        if len(scores) == 0:
-            return boxes, scores, cls_ids
-        thr = self._density_conf(len(scores))
-        if thr is None:
-            return boxes, scores, cls_ids
-        keep = scores >= np.float32(thr)
+        return padded, ratio, (dw, dh)
+
+    def _decode(self, output, ratio, pad, original_size):
+        rows = output[0]
+        boxes = rows[:, :4].astype(np.float32, copy=True)
+        scores = rows[:, 4].astype(np.float32, copy=False)
+        raw_classes = rows[:, 5]
+        if (scores < 0).any() or (scores > 1).any():
+            raise InferenceInvariantError("e2e confidence outside [0, 1]")
+        rounded = np.rint(raw_classes)
+        if not np.array_equal(raw_classes, rounded):
+            raise InferenceInvariantError("e2e model class ids must be exact integers")
+        model_classes = rounded.astype(np.int64)
+        if (model_classes < 0).any() or (model_classes >= len(self.model_names)).any():
+            raise InferenceInvariantError("e2e model class id outside range")
+
+        active = scores > 0.0
+        reversed_rows = active & ((boxes[:, 2] < boxes[:, 0]) | (boxes[:, 3] < boxes[:, 1]))
+        if reversed_rows.any():
+            raise InferenceInvariantError("e2e output contains reversed boxes")
+
+        floor = min([self.conf_threshold] + [float(c) for _, c in self.conf_steps])
+        keep = active & (model_classes == 0) & (scores >= np.float32(floor))
         if not keep.any():
-            keep[int(np.argmax(scores))] = True
-        return boxes[keep], scores[keep], cls_ids[keep]
-
-    def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray,
-                           cls_ids: np.ndarray, orig_size: tuple[int, int]
-                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """sane filter -> top-k cap -> per-class NMS -> max_det cap -> dedup."""
-        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        if len(scores) > self.pre_nms_topk:
-            top = np.argpartition(-scores, self.pre_nms_topk)[: self.pre_nms_topk]
-            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-        if len(boxes) > 1:
-            if self.use_soft_nms:
-                keep, scores = self._per_class_soft_nms(boxes, scores, cls_ids)
-                boxes, cls_ids = boxes[keep], cls_ids[keep]
-            else:
-                keep = self._per_class_hard_nms(boxes, scores, cls_ids)
-                boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
-        if len(scores) > self.max_det:
-            top = np.argsort(-scores)[: self.max_det]
-            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-        if len(boxes) > 1:
-            boxes, scores, cls_ids = self._cross_class_dedup_op(
-                boxes, scores, cls_ids, self.cross_iou_thresh)
-        return boxes, scores, cls_ids
-
-    # ── Coordinate un-mapping (shared by both decode paths) ──────────────────
+            return np.empty((0, 4), np.float32), np.empty((0,), np.float32)
+        boxes, scores = boxes[keep], scores[keep]
 
-    def _unmap_and_finish(self, boxes: np.ndarray, scores: np.ndarray,
-                          cls_ids: np.ndarray, meta: dict
-                          ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Undo letterbox + square-pad, drop pad-center boxes, clip, post-process."""
-        pad_w, pad_h = meta["pad"]
-        ratio = meta["ratio"]
-        orig_w, orig_h = meta["orig_size"]
-        extra_left, extra_right = meta["extra_left"], meta["extra_right"]
-
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        if extra_left:
-            boxes[:, [0, 2]] -= extra_left
-        if extra_left or extra_right:
+        boxes[:, [0, 2]] = (boxes[:, [0, 2]] - pad[0]) / ratio
+        boxes[:, [1, 3]] = (boxes[:, [1, 3]] - pad[1]) / ratio
+        width, height = original_size
+        if self.scale_w != 1.0 or self.scale_h != 1.0:
             cx = (boxes[:, 0] + boxes[:, 2]) * 0.5
-            inside = (cx >= 0) & (cx <= orig_w)
-            boxes, scores, cls_ids = boxes[inside], scores[inside], cls_ids[inside]
-            if len(boxes) == 0:
-                return boxes, scores, cls_ids
-        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
-        return self._per_view_pipeline(boxes, scores, cls_ids, (orig_w, orig_h))
-
-    # ── Decoding ─────────────────────────────────────────────────────────────
-
-    def _decode_final_dets(self, preds: np.ndarray, meta: dict
-                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """End2end output rows [x1, y1, x2, y2, conf, cls_id] (NMS in graph)."""
-        empty = (np.empty((0, 4), np.float32), np.empty(0, np.float32), np.empty(0, np.int32))
-        if preds.ndim == 3 and preds.shape[0] == 1:
-            preds = preds[0]
-        if preds.ndim != 2 or preds.shape[1] < 6:
-            raise ValueError(f"Unexpected final-det output shape: {preds.shape}")
-        boxes = np.asarray(preds[:, :4], dtype=np.float32)
-        scores = np.asarray(preds[:, 4], dtype=np.float32)
-        cls_ids = self.cls_remap[preds[:, 5].astype(np.int32)]
-        keep = self._conf_filter_mask(scores, cls_ids, meta["extra_left"])
-        boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
-        if len(boxes) == 0:
-            return empty
-        return self._unmap_and_finish(boxes, scores, cls_ids, meta)
-
-    def _decode_raw_yolo(self, preds: np.ndarray, meta: dict
-                         ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Raw YOLO output [1,C,N] or [1,N,C]; xywh + per-class scores."""
-        empty = (np.empty((0, 4), np.float32), np.empty(0, np.float32), np.empty(0, np.int32))
-        if preds.ndim != 3 or preds.shape[0] != 1:
-            raise ValueError(f"Unexpected raw output shape: {preds.shape}")
-        preds = preds[0]
-        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
-            preds = preds.T
-        if preds.ndim != 2 or preds.shape[1] < 5:
-            raise ValueError(f"Unexpected normalized raw shape: {preds.shape}")
-        boxes_xywh = preds[:, :4].astype(np.float32)
-        cls_part = preds[:, 4:].astype(np.float32)
-        if cls_part.shape[1] == 1:
-            scores = cls_part[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
-            scores = cls_part[np.arange(len(cls_part)), cls_ids]
-        cls_ids = self.cls_remap[cls_ids]
-        keep = self._conf_filter_mask(scores, cls_ids, meta["extra_left"])
-        boxes_xywh, scores, cls_ids = boxes_xywh[keep], scores[keep], cls_ids[keep]
-        if len(boxes_xywh) == 0:
-            return empty
-        boxes = self._xywh_to_xyxy(boxes_xywh)
-        return self._unmap_and_finish(boxes, scores, cls_ids, meta)
-
-    def _decode(self, output: np.ndarray, meta: dict
-                ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, meta)
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
-            return self._decode_final_dets(output, meta)
-        return self._decode_raw_yolo(output, meta)
-
-    # ── Inference ────────────────────────────────────────────────────────────
-
-    def _predict_single_arrays(self, image: np.ndarray
-                               ) -> tuple[np.ndarray, np.ndarray, np.ndarray, dict]:
+            cy = (boxes[:, 1] + boxes[:, 3]) * 0.5
+            hw = (boxes[:, 2] - boxes[:, 0]) * 0.5 * np.float32(self.scale_w)
+            hh = (boxes[:, 3] - boxes[:, 1]) * 0.5 * np.float32(self.scale_h)
+            boxes = np.stack([cx - hw, cy - hh, cx + hw, cy + hh], 1).astype(np.float32)
+        boxes[:, [0, 2]] = np.clip(boxes[:, [0, 2]], 0, width - 1)
+        boxes[:, [1, 3]] = np.clip(boxes[:, [1, 3]], 0, height - 1)
+
+        bw = boxes[:, 2] - boxes[:, 0]
+        bh = boxes[:, 3] - boxes[:, 1]
+        aspect = np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6))
+        sane = (
+            (bw >= self.min_side)
+            & (bh >= self.min_side)
+            & (bw * bh <= self.max_area_frac * float(width * height))
+            & (aspect <= self.max_aspect_ratio)
+        )
+        boxes, scores = boxes[sane], scores[sane]
+        if not len(scores):
+            return boxes, scores
+        order = np.argsort(-scores, kind="stable")[: self.max_det]
+        boxes, scores = boxes[order], scores[order]
+        gate = _conf_for(len(scores)) if self.conf_steps else self.conf_threshold
+        emit = scores >= np.float32(gate)
+        return boxes[emit], scores[emit]
+
+    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
         if not isinstance(image, np.ndarray) or image.ndim != 3 or image.shape[2] != 3:
-            raise ValueError(f"Expected HWC BGR image, got {type(image)} {getattr(image,'shape',None)}")
+            raise ValueError(f"expected HWC 3-channel image, got {getattr(image, 'shape', type(image))}")
         if image.dtype != np.uint8:
             image = image.astype(np.uint8)
-        inp, meta = self._preprocess(image)
-        outputs = self.session.run(self.output_names, {self.input_name: inp})
-        boxes, scores, cls_ids = self._decode(outputs[0], meta)
-        return boxes, scores, cls_ids, meta
-
-    @staticmethod
-    def _to_boxes(boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray,
-                  orig_size: tuple[int, int]) -> list[BoundingBox]:
-        if len(boxes) == 0:
-            return []
-        orig_w, orig_h = orig_size
-        valid = (boxes[:, 2] > boxes[:, 0]) & (boxes[:, 3] > boxes[:, 1])
-        if not valid.all():
-            boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid]
-            if len(boxes) == 0:
-                return []
-        # floor/ceil + clamp for every box in six numpy calls; the per-box
-        # math.floor/max/min chain was the bulk of this function.
-        x1 = np.clip(np.floor(boxes[:, 0]), 0, orig_w).astype(np.int64).tolist()
-        y1 = np.clip(np.floor(boxes[:, 1]), 0, orig_h).astype(np.int64).tolist()
-        x2 = np.clip(np.ceil(boxes[:, 2]), 0, orig_w).astype(np.int64).tolist()
-        y2 = np.clip(np.ceil(boxes[:, 3]), 0, orig_h).astype(np.int64).tolist()
-        confs = np.clip(scores, 0.0, 1.0).tolist()
-        clss = cls_ids.astype(np.int64).tolist()
-        return [BoundingBox(x1=a, y1=b, x2=c, y2=d, cls_id=k, conf=s)
-                for a, b, c, d, k, s in zip(x1, y1, x2, y2, clss, confs)]
-
-    def _infer_single(self, image: np.ndarray) -> list[BoundingBox]:
-        boxes, scores, cls_ids, meta = self._predict_single_arrays(image)
-        if len(boxes) == 0:
-            return []
-        if self.use_cluster_boost and len(boxes) > 1:
-            scores = self._max_score_per_cluster(
-                boxes, cls_ids, boxes, scores, cls_ids, self._avg_iou)
-        boxes, scores, cls_ids = self._apply_density_gate(boxes, scores, cls_ids)
-        return self._to_boxes(boxes, scores, cls_ids, meta["orig_size"])
-
-    def _infer_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        """Horizontal-flip TTA: union of original + flipped, per-class NMS,
-        same-class cluster-max boost, dedup."""
-        b0, s0, c0, meta = self._predict_single_arrays(image)
-        w = image.shape[1]
-        bf, sf, cf, _ = self._predict_single_arrays(cv2.flip(image, 1))
-        if len(bf):
-            bf = bf.copy()
-            bf[:, [0, 2]] = w - bf[:, [2, 0]]  # mirror x back to original coords
-        coords = np.concatenate([b0, bf], axis=0) if len(bf) else b0
-        scores = np.concatenate([s0, sf], axis=0) if len(sf) else s0
-        cls_ids = np.concatenate([c0, cf], axis=0) if len(cf) else c0
-        if len(coords) == 0:
-            return []
-        keep = self._per_class_hard_nms(coords, scores, cls_ids)
-        if len(keep) == 0:
-            return []
-        if len(keep) > self.max_det:
-            keep = keep[np.argsort(-scores[keep])[: self.max_det]]
-        boosted = self._max_score_per_cluster(
-            coords[keep], cls_ids[keep], coords, scores, cls_ids, self._avg_iou)
-        kb, kc = coords[keep], cls_ids[keep]
-        if len(kb) > 1:
-            kb, boosted, kc = self._cross_class_dedup_op(kb, boosted, kc, self.cross_iou_thresh)
-        kb, boosted, kc = self._apply_density_gate(kb, boosted, kc)
-        return self._to_boxes(kb, boosted, kc, meta["orig_size"])
+        original_height, original_width = image.shape[:2]
+        padded, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
+        blob = cv2.dnn.blobFromImage(padded, scalefactor=1 / 255.0, swapRB=True)
+        outputs = self.session.run(self.output_names, {self.input_name: blob})
+        output = outputs[0]
+        if not np.isfinite(output).all():
+            raise InferenceInvariantError("model output must be finite everywhere")
+        if list(output.shape) != [1, OUTPUT_ROWS, OUTPUT_COLUMNS]:
+            raise ValueError(f"unexpected output shape {list(output.shape)}")
+        boxes, scores = self._decode(output, ratio, pad, (original_width, original_height))
+        return [
+            BoundingBox(
+                x1=int(math.floor(b[0])), y1=int(math.floor(b[1])),
+                x2=int(math.ceil(b[2])), y2=int(math.ceil(b[3])),
+                cls_id=0, conf=float(s),
+            )
+            for b, s in zip(boxes, scores)
+            if b[2] > b[0] and b[3] > b[1]
+        ]
+
+    def _warmup(self, iterations: int) -> None:
+        dummy = np.zeros((720, 1280, 3), dtype=np.uint8)
+        original = self.strict_inference
+        self.strict_inference = True
+        try:
+            for _ in range(iterations):
+                self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0)
+        except Exception as exc:
+            raise RuntimeError(f"warmup failed, refusing to serve: {type(exc).__name__}: {exc}") from exc
+        finally:
+            self.strict_inference = original
 
-    def predict_batch(self, batch_images: list[ndarray], offset: int,
-                      n_keypoints: int) -> list[TVFrameResult]:
+    def predict_batch(self, batch_images, offset: int, n_keypoints: int) -> list[TVFrameResult]:
+        _ = n_keypoints
         results: list[TVFrameResult] = []
-        for idx, image in enumerate(batch_images):
+        for index, image in enumerate(batch_images):
+            frame_id = offset + index
             try:
-                boxes = self._infer_tta(image) if self.use_tta else self._infer_single(image)
-            except Exception as e:
-                print(f"Inference failed for frame {offset + idx}: {e}")
+                boxes = self._predict_single(image)
+            except Exception as exc:
+                if self.strict_inference:
+                    raise
+                self.inference_failures += 1
+                print(json.dumps({
+                    "event": "inference_failure",
+                    "frame_id": frame_id,
+                    "error_type": type(exc).__name__,
+                    "error": str(exc),
+                    "inference_failures": self.inference_failures,
+                }, sort_keys=True))
                 boxes = []
-            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
-            results.append(TVFrameResult(frame_id=offset + idx, boxes=boxes, keypoints=keypoints))
+            results.append(TVFrameResult(frame_id=frame_id, boxes=boxes, keypoints=[]))
         return results
+
+    def __repr__(self) -> str:
+        return f"E2EONNXRuntime(providers={self.session.get_providers()})"

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "miner-element-adapter"
 version = "0.1.0"
-requires-python = ">=3.9"
+requires-python = ">=3.11"
 
 dependencies = [
   "numpy>=1.23",

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ec627a7e406e2ba42c91f738bd73863f317a6b63cf3f6b5c713eb13a8184a4d5
-size 10654740
+oid sha256:f2adc7fc20e19817198873a52dabfbac62d2e312b75253a54622b7aeda7f7bb8
+size 9860783