scorevision: push artifact

miner.py

@@ -1,15 +1,3 @@
-"""Plate-detection miner — plate_v6 + consensus-TTA inference.
-
-Weights: plate_v6 (resumed plate_v5 + difficulty-weighted scraped real challenges
-+ synth CCTV; 18 epochs, peak val mAP50 0.930).
-
-Inference (smile0123-style consensus-TTA, our bench winner at gated 0.443):
-  - low conf (0.15) for high recall, super-high-conf (>=0.90) passes directly
-  - hflip cross-view consensus: low-conf boxes must match a flipped-view box at IoU>=0.01
-  - final hard-NMS at iou=0.32, max_det=150
-Bench on 221-shard live pseudo-GT pool: gated 0.443  mAP 0.975  fp/img 0.25  ms_p95 ~150 (A4000)
-On pro_6000 + TEE expect ~2-3s p95 including network/attest overhead.
-"""
 from pathlib import Path
 import math
 
@@ -45,7 +33,8 @@ class Miner:
         if cn_path.is_file():
             lines = cn_path.read_text(encoding="utf-8").splitlines()
             self.class_names = [
-                ln.strip() for ln in lines
+                ln.strip()
+                for ln in lines
                 if ln.strip() and not ln.strip().startswith("#")
             ]
         else:
@@ -58,11 +47,15 @@ class Miner:
         except Exception as e:
             print(f"preload_dlls failed: {e}")
 
+        print("ORT available providers BEFORE session:", ort.get_available_providers())
+
         try:
             import torch
             if torch.cuda.is_available():
                 print(f"GPU: {torch.cuda.get_device_name(0)}")
-                print(f"GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
+                print(f"GPU memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
+            else:
+                print("GPU: CUDA not available via torch")
         except Exception as e:
             print(f"GPU detection failed: {e}")
 
@@ -71,17 +64,21 @@ class Miner:
 
         try:
             self.session = ort.InferenceSession(
-                str(model_path), sess_options=sess_options,
+                str(model_path),
+                sess_options=sess_options,
                 providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
             )
-            print("Created ORT session with CUDA provider")
+            print("Created ORT session with preferred CUDA provider list")
         except Exception as e:
             print(f"CUDA session creation failed, falling back to CPU: {e}")
             self.session = ort.InferenceSession(
-                str(model_path), sess_options=sess_options,
+                str(model_path),
+                sess_options=sess_options,
                 providers=["CPUExecutionProvider"],
             )
 
+        print("ORT session providers:", self.session.get_providers())
+
         for inp in self.session.get_inputs():
             print("INPUT:", inp.name, inp.shape, inp.type)
         for out in self.session.get_outputs():
@@ -90,55 +87,75 @@ class Miner:
         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
-        self.input_dtype = (
-            np.float16 if "float16" in self.session.get_inputs()[0].type
-            else np.float32
-        )
+
         self.input_height = self._safe_dim(self.input_shape[2], default=SIZE)
         self.input_width = self._safe_dim(self.input_shape[3], default=SIZE)
 
-        # Consensus-TTA preset (bench winner — gated 0.443)
-        self.conf_thres = 0.15        # low — collect MANY candidates
-        self.conf_high = 0.90         # >= this → pass through without TTA match
-        self.tta_match_iou = 0.01     # very permissive cross-view match
-        self.iou_thres = 0.32         # final hard-NMS
-        self.max_det = 150
+        # Primary pass: alfred001 weights + lower conf (bench-verified +0.007 gated vs alfred stock c=0.26)
+        self.conf_thres = 0.22
+        self.iou_thres = 0.39
+        self.sigma = 0.465
+        self.max_det = 300
+
+        # Conditional tile-pass (trimmed for latency: no hflip, tighter sparse)
+        self.sparse_threshold = 3       # fire tiles only if primary returns < this
+        self.tile_conf = 0.57
+        self.tile_overlap = 0.20
+        self.novelty_iou = 0.10
+        self.final_max_det = 17
+        self.tile_use_hflip = False     # skip hflip tile pass to save ~4 forwards
+
+        self.use_tta = True
 
-        print(f"Preset: conf={self.conf_thres} conf_high={self.conf_high} "
-              f"tta_match_iou={self.tta_match_iou} iou={self.iou_thres} max_det={self.max_det}")
         print(f"ONNX model loaded from: {model_path}")
+        print(f"ONNX providers: {self.session.get_providers()}")
+        print(f"ONNX input: name={self.input_name}, shape={self.input_shape}")
 
     def __repr__(self) -> str:
-        return f"ONNXRuntime(providers={self.session.get_providers()})"
+        return (
+            f"ONNXRuntime(session={type(self.session).__name__}, "
+            f"providers={self.session.get_providers()})"
+        )
 
     @staticmethod
     def _safe_dim(value, default: int) -> int:
         return value if isinstance(value, int) and value > 0 else default
 
-    # ---------- preprocessing ----------
-    def _letterbox(self, image, new_shape, color=(114, 114, 114)):
+    # ---------- image 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):
+        resized_w = int(round(w * ratio))
+        resized_h = int(round(h * ratio))
+        if (resized_w, resized_h) != (w, h):
             interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            image = cv2.resize(image, (rw, rh), interpolation=interp)
-        dw, dh = (new_w - rw) / 2.0, (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))
-        padded = cv2.copyMakeBorder(image, top, bottom, left, right,
-                                    borderType=cv2.BORDER_CONSTANT, value=color)
+            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
+        dw = (new_w - resized_w) / 2.0
+        dh = (new_h - resized_h) / 2.0
+        left = int(round(dw - 0.1))
+        right = int(round(dw + 0.1))
+        top = int(round(dh - 0.1))
+        bottom = int(round(dh + 0.1))
+        padded = cv2.copyMakeBorder(
+            image, top, bottom, left, right,
+            borderType=cv2.BORDER_CONSTANT, value=color,
+        )
         return padded, ratio, (dw, dh)
 
-    def _preprocess(self, image):
+    def _preprocess(self, image: ndarray):
         img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
         img = np.transpose(img, (2, 0, 1))[None, ...]
-        return np.ascontiguousarray(img, dtype=self.input_dtype), ratio, pad
+        return np.ascontiguousarray(img, dtype=np.float32), ratio, pad
 
     @staticmethod
-    def _clip_boxes(boxes, image_size):
+    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)
@@ -146,32 +163,20 @@ class Miner:
         boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
         return boxes
 
-    # ---------- detection helpers ----------
+    # ---------- NMS primitives ----------
     @staticmethod
-    def _iou_one_to_many(box, boxes):
-        if len(boxes) == 0:
-            return np.zeros(0, dtype=np.float32)
-        xx1 = np.maximum(box[0], boxes[:, 0])
-        yy1 = np.maximum(box[1], boxes[:, 1])
-        xx2 = np.minimum(box[2], boxes[:, 2])
-        yy2 = np.minimum(box[3], boxes[:, 3])
-        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-        ai = max(0.0, (box[2] - box[0]) * (box[3] - box[1]))
-        aj = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0])
-              * np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
-        return inter / (ai + aj - inter + 1e-7)
-
-    @staticmethod
-    def _hard_nms(boxes, scores, iou_thresh, max_det):
+    def _hard_nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> np.ndarray:
         N = len(boxes)
         if N == 0:
             return np.array([], dtype=np.intp)
+        boxes = np.asarray(boxes, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
         order = np.argsort(-scores)
-        keep = []
+        keep: list[int] = []
         while len(order):
             i = int(order[0])
             keep.append(i)
-            if len(order) == 1 or len(keep) >= max_det:
+            if len(order) == 1:
                 break
             rest = order[1:]
             xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
@@ -179,13 +184,65 @@ class Miner:
             xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
             yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
             inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            ai = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
-            ar = (boxes[rest, 2] - boxes[rest, 0]) * (boxes[rest, 3] - boxes[rest, 1])
-            iou = inter / (ai + ar - inter + 1e-7)
+            area_i = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            area_r = (boxes[rest, 2] - boxes[rest, 0]) * (boxes[rest, 3] - boxes[rest, 1])
+            iou = inter / (area_i + area_r - inter + 1e-7)
             order = rest[iou <= iou_thresh]
         return np.array(keep, dtype=np.intp)
 
-    def _raw_dets(self, image, conf):
+    def _soft_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        sigma: float,
+        score_thresh: float = 0.01,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        N = len(boxes)
+        if N == 0:
+            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
+        boxes = boxes.astype(np.float32, copy=True)
+        scores = scores.astype(np.float32, copy=True)
+        order = np.arange(N)
+        for i in range(N):
+            max_pos = i + int(np.argmax(scores[i:]))
+            boxes[[i, max_pos]] = boxes[[max_pos, i]]
+            scores[[i, max_pos]] = scores[[max_pos, i]]
+            order[[i, max_pos]] = order[[max_pos, i]]
+            if i + 1 >= N:
+                break
+            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            area_i = float(
+                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            )
+            areas_j = (
+                np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
+                * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1])
+            )
+            iou = inter / (area_i + areas_j - inter + 1e-7)
+            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
+        mask = scores > score_thresh
+        return order[mask], scores[mask]
+
+    @staticmethod
+    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
+        if len(boxes) == 0:
+            return np.zeros(0, dtype=np.float32)
+        xx1 = np.maximum(box[0], boxes[:, 0])
+        yy1 = np.maximum(box[1], boxes[:, 1])
+        xx2 = np.minimum(box[2], boxes[:, 2])
+        yy2 = np.minimum(box[3], boxes[:, 3])
+        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+        area_a = max(0.0, (box[2] - box[0]) * (box[3] - box[1]))
+        area_b = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
+        return inter / (area_a + area_b - inter + 1e-7)
+
+    # ---------- raw-dets helper ----------
+    def _raw_dets(self, image: ndarray, conf: float) -> np.ndarray:
+        """Run a single forward pass and return [N, 5] dets in ORIGINAL image coords."""
         x, ratio, (dw, dh) = self._preprocess(image)
         out = self.session.run(self.output_names, {self.input_name: x})[0]
         if out.ndim == 3:
@@ -205,51 +262,111 @@ class Miner:
         boxes = self._clip_boxes(boxes, (ow, oh))
         return np.concatenate([boxes, scores[:, None]], axis=1)
 
-    # ---------- consensus-TTA prediction ----------
-    def _predict_consensus_tta(self, image):
-        d_o = self._raw_dets(image, self.conf_thres)
+    # ---------- primary pass: soft-NMS + hflip TTA ----------
+    def _primary(self, image: ndarray) -> np.ndarray:
+        d1 = self._raw_dets(image, self.conf_thres)
         flipped = cv2.flip(image, 1)
-        d_f = self._raw_dets(flipped, self.conf_thres)
-        if len(d_f):
+        d2 = self._raw_dets(flipped, self.conf_thres)
+        if len(d2):
             w = image.shape[1]
-            d_f = np.stack([w - d_f[:, 2], d_f[:, 1], w - d_f[:, 0],
-                            d_f[:, 3], d_f[:, 4]], axis=1)
-
-        accepted_boxes = []
-        accepted_scores = []
-        # Original-view candidates
-        for i in range(len(d_o)):
-            s = float(d_o[i, 4])
-            if s >= self.conf_high:
-                accepted_boxes.append(d_o[i, :4])
-                accepted_scores.append(s)
-            elif len(d_f) > 0:
-                ious = self._iou_one_to_many(d_o[i, :4], d_f[:, :4])
-                j = int(np.argmax(ious))
-                if ious[j] >= self.tta_match_iou:
-                    fused = max(s, float(d_f[j, 4]))
-                    accepted_boxes.append(d_o[i, :4])
-                    accepted_scores.append(fused)
-        # Flip-view high-conf boxes that original missed
-        for i in range(len(d_f)):
-            s = float(d_f[i, 4])
-            if s < self.conf_high:
-                continue
-            if len(d_o) == 0:
-                accepted_boxes.append(d_f[i, :4])
-                accepted_scores.append(s)
-                continue
-            ious = self._iou_one_to_many(d_f[i, :4], d_o[:, :4])
-            if np.max(ious) < self.tta_match_iou:
-                accepted_boxes.append(d_f[i, :4])
-                accepted_scores.append(s)
-
-        if not accepted_boxes:
+            x1 = w - d2[:, 2]
+            x2 = w - d2[:, 0]
+            d2 = np.stack([x1, d2[:, 1], x2, d2[:, 3], d2[:, 4]], axis=1)
+        all_d = np.concatenate([d1, d2], axis=0) if len(d2) else d1
+        if len(all_d) == 0:
+            return np.zeros((0, 5), dtype=np.float32)
+        # soft-NMS, then hard-NMS
+        keep_idx, scores = self._soft_nms(all_d[:, :4].copy(), all_d[:, 4].copy(), sigma=self.sigma)
+        if len(keep_idx) == 0:
             return np.zeros((0, 5), dtype=np.float32)
-        boxes = np.array(accepted_boxes, dtype=np.float32)
-        scores = np.array(accepted_scores, dtype=np.float32)
-        keep = self._hard_nms(boxes, scores, self.iou_thres, self.max_det)
-        return np.concatenate([boxes[keep], scores[keep][:, None]], axis=1)
+        merged = np.concatenate([all_d[keep_idx, :4], scores[:, None]], axis=1)
+        keep = self._hard_nms(merged[:, :4], merged[:, 4], self.iou_thres)
+        merged = merged[keep]
+        if len(merged) > self.max_det:
+            merged = merged[np.argsort(-merged[:, 4])[: self.max_det]]
+        return merged
+
+    # ---------- conditional tile pass ----------
+    def _tile_augment(self, image: ndarray, primary: np.ndarray) -> np.ndarray:
+        """Run 2x2 overlapping tiles + hflip, novelty-merge into primary."""
+        oh, ow = image.shape[:2]
+        tw, th = ow // 2, oh // 2
+        ox, oy = int(tw * self.tile_overlap), int(th * self.tile_overlap)
+        tiles = [
+            (0, 0, min(ow, tw + ox), min(oh, th + oy)),
+            (max(0, tw - ox), 0, ow, min(oh, th + oy)),
+            (0, max(0, th - oy), min(ow, tw + ox), oh),
+            (max(0, tw - ox), max(0, th - oy), ow, oh),
+        ]
+        collected: list[np.ndarray] = []
+        for x1, y1, x2, y2 in tiles:
+            crop = image[y1:y2, x1:x2]
+            if crop.size == 0:
+                continue
+            d = self._raw_dets(crop, self.tile_conf)
+            if len(d):
+                d[:, 0] += x1
+                d[:, 1] += y1
+                d[:, 2] += x1
+                d[:, 3] += y1
+                collected.append(d)
+
+        # hflip tile pass (skipped when tile_use_hflip=False — saves 4 ONNX forwards)
+        if self.tile_use_hflip:
+            flipped = cv2.flip(image, 1)
+            for x1, y1, x2, y2 in tiles:
+                fx1 = ow - x2
+                fx2 = ow - x1
+                if fx2 <= fx1:
+                    continue
+                crop = flipped[y1:y2, fx1:fx2]
+                if crop.size == 0:
+                    continue
+                d = self._raw_dets(crop, self.tile_conf)
+                if len(d):
+                    d_un = d.copy()
+                    d_un[:, 0] = (ow - (d[:, 2] + fx1))
+                    d_un[:, 2] = (ow - (d[:, 0] + fx1))
+                    d_un[:, 1] = d[:, 1] + y1
+                    d_un[:, 3] = d[:, 3] + y1
+                    collected.append(d_un)
+
+        if not collected:
+            return primary
+
+        tile_dets = np.concatenate(collected, axis=0)
+        keep = self._hard_nms(tile_dets[:, :4], tile_dets[:, 4], 0.5)
+        tile_dets = tile_dets[keep]
+
+        # Novelty: drop tile boxes that overlap any primary box at IoU >= novelty_iou
+        if len(primary) > 0 and len(tile_dets) > 0:
+            mask = np.ones(len(tile_dets), dtype=bool)
+            for i in range(len(tile_dets)):
+                ious = self._box_iou_one_to_many(tile_dets[i, :4], primary[:, :4])
+                if len(ious) and np.max(ious) >= self.novelty_iou:
+                    mask[i] = False
+            tile_dets = tile_dets[mask]
+
+        if len(tile_dets) == 0:
+            return primary
+
+        # Sanity filter: min/max size, aspect ratio
+        w = tile_dets[:, 2] - tile_dets[:, 0]
+        h = tile_dets[:, 3] - tile_dets[:, 1]
+        area = w * h
+        ar = np.maximum(w / np.maximum(h, 1e-6), h / np.maximum(w, 1e-6))
+        img_area = float(ow * oh)
+        ok = (w >= 7) & (h >= 7) & (area >= 85) & (area <= 0.5 * img_area) & (ar <= 10.0)
+        tile_dets = tile_dets[ok]
+        if len(tile_dets) == 0:
+            return primary
+
+        merged = np.concatenate([primary, tile_dets], axis=0)
+        keep = self._hard_nms(merged[:, :4], merged[:, 4], self.iou_thres)
+        merged = merged[keep]
+        if len(merged) > self.final_max_det:
+            merged = merged[np.argsort(-merged[:, 4])[: self.final_max_det]]
+        return merged
 
     # ---------- single-image predict ----------
     def _predict_single(self, image: ndarray) -> list[BoundingBox]:
@@ -260,26 +377,36 @@ class Miner:
         if image.dtype != np.uint8:
             image = image.astype(np.uint8)
 
-        dets = self._predict_consensus_tta(image)
+        primary = self._primary(image)
+        if len(primary) < self.sparse_threshold:
+            dets = self._tile_augment(image, primary)
+        else:
+            dets = primary
 
         results: list[BoundingBox] = []
         for row in dets:
             x1, y1, x2, y2, conf = row.tolist()
             if x2 <= x1 or y2 <= y1:
                 continue
-            results.append(BoundingBox(
-                x1=int(math.floor(x1)),
-                y1=int(math.floor(y1)),
-                x2=int(math.ceil(x2)),
-                y2=int(math.ceil(y2)),
-                cls_id=0,
-                conf=float(conf),
-            ))
+            results.append(
+                BoundingBox(
+                    x1=int(math.floor(x1)),
+                    y1=int(math.floor(y1)),
+                    x2=int(math.ceil(x2)),
+                    y2=int(math.ceil(y2)),
+                    cls_id=0,
+                    conf=float(conf),
+                )
+            )
         return results
 
     # ---------- chute entrypoint ----------
-    def predict_batch(self, batch_images: list[ndarray], offset: int,
-                      n_keypoints: int) -> list[TVFrameResult]:
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
         results: list[TVFrameResult] = []
         for frame_number_in_batch, image in enumerate(batch_images):
             try:
@@ -287,9 +414,11 @@ class Miner:
             except Exception as e:
                 print(f"Inference failed for frame {offset + frame_number_in_batch}: {e}")
                 boxes = []
-            results.append(TVFrameResult(
-                frame_id=offset + frame_number_in_batch,
-                boxes=boxes,
-                keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
-            ))
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + frame_number_in_batch,
+                    boxes=boxes,
+                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
+                )
+            )
         return results