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chute_config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu[cuda,cudnn]>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
+    - pip install torch torchvision
+    - echo "beverage v4 2026-04-30T13:45 tee+pro_6000+torch"
+
+NodeSelector:
+  gpu_count: 1
+  include:
+    - pro_6000
+
+Chute:
+  tee: true
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 288000

class_names.txt

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+cup
+bottle
+can

miner.py

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+"""TurboVision beverage detection miner — score-beverage-v3.
+
+YOLO11s @ 1280x1280, 3-class beverage detection (bottle/can/cup),
+ONNX with end-to-end NMS baked in (output [1, 300, 6] = x1, y1, x2, y2, conf, cls).
+
+Inference pipeline (v3):
+  1) Primary forward pass on the full image.
+  2) Hflip TTA: forward on horizontally-flipped image, transform boxes back.
+  3) Per-class hard-NMS to merge primary + flip outputs.
+  4) Cross-class IoU dedup (suppresses same physical object getting two class labels).
+  5) Consensus-confidence boost: when both views agree on a cluster, take the max
+     score so true-positives rank higher in the validator's PR curve.
+  6) Sanity filter (min size, aspect ratio).
+"""
+
+from pathlib import Path
+import math
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from numpy import ndarray
+from pydantic import BaseModel
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+
+class Miner:
+    def __init__(self, path_hf_repo: Path) -> None:
+        model_path = path_hf_repo / "weights.onnx"
+
+        cn_path = model_path.with_name("class_names.txt")
+        if cn_path.is_file():
+            self.class_names = [
+                ln.strip()
+                for ln in cn_path.read_text(encoding="utf-8").splitlines()
+                if ln.strip() and not ln.strip().startswith("#")
+            ]
+        else:
+            self.class_names = ["cup", "bottle", "can"]
+        self.cls_remap = np.arange(len(self.class_names), dtype=np.int32)
+
+        print("ORT version:", ort.__version__)
+        try:
+            ort.preload_dlls()
+            print("✅ onnxruntime.preload_dlls() success")
+        except Exception as e:
+            print(f"⚠️ preload_dlls failed: {e}")
+        print("ORT available providers BEFORE session:", ort.get_available_providers())
+
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        try:
+            self.session = ort.InferenceSession(
+                str(model_path),
+                sess_options=sess_options,
+                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+            )
+            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,
+                providers=["CPUExecutionProvider"],
+            )
+        print("ORT session providers:", self.session.get_providers())
+
+        inp = self.session.get_inputs()[0]
+        self.input_name = inp.name
+        self.output_names = [o.name for o in self.session.get_outputs()]
+        self.input_shape = inp.shape
+        self.input_dtype = np.float16 if "float16" in inp.type else np.float32
+
+        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
+        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
+
+        self.conf_thres = 0.20
+        self.iou_thres = 0.5
+        self.cross_iou_thresh = 0.7
+        self.max_det = 300
+        self.use_tta = True
+
+        # Sanity filter — reject obviously bad boxes
+        self.min_box_area = 6 * 6
+        self.min_side = 4
+        self.max_aspect_ratio = 8.0
+        self.max_box_area_ratio = 0.95
+
+        print(f"✅ ONNX loaded: {model_path}")
+        print(f"✅ providers: {self.session.get_providers()}")
+        print(f"✅ input: name={self.input_name}, shape={self.input_shape}, dtype={self.input_dtype}")
+        print(f"✅ classes: {self.class_names}")
+        print(f"✅ config: conf={self.conf_thres}, iou={self.iou_thres}, "
+              f"cross_iou={self.cross_iou_thresh}, TTA={self.use_tta}")
+
+    def __repr__(self) -> str:
+        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
+
+    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)
+        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, (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: ndarray):
+        orig_h, orig_w = image.shape[:2]
+        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = img.astype(self.input_dtype) / 255.0
+        img = np.transpose(img, (2, 0, 1))[None, ...]
+        img = np.ascontiguousarray(img)
+        return img, ratio, pad, (orig_w, orig_h)
+
+    @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
+
+    def _filter_sane_boxes(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        orig_size: tuple[int, int],
+    ):
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+        orig_w, orig_h = orig_size
+        image_area = float(orig_w * orig_h)
+        keep = []
+        for i, box in enumerate(boxes):
+            x1, y1, x2, y2 = box.tolist()
+            bw = x2 - x1
+            bh = y2 - y1
+            if bw <= 0 or bh <= 0:
+                continue
+            if bw < self.min_side or bh < self.min_side:
+                continue
+            area = bw * bh
+            if area < self.min_box_area:
+                continue
+            if area > self.max_box_area_ratio * image_area:
+                continue
+            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
+            if ar > self.max_aspect_ratio:
+                continue
+            keep.append(i)
+        if not keep:
+            return (
+                np.empty((0, 4), dtype=np.float32),
+                np.empty((0,), dtype=np.float32),
+                np.empty((0,), dtype=np.int32),
+            )
+        k = np.array(keep, dtype=np.intp)
+        return boxes[k], scores[k], cls_ids[k]
+
+    @staticmethod
+    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)[::-1]
+        keep: list[int] = []
+        suppressed = np.zeros(N, dtype=bool)
+        for i in range(N):
+            idx = order[i]
+            if suppressed[idx]:
+                continue
+            keep.append(int(idx))
+            bi = boxes[idx]
+            for k in range(i + 1, N):
+                jdx = order[k]
+                if suppressed[jdx]:
+                    continue
+                bj = boxes[jdx]
+                xx1 = max(bi[0], bj[0])
+                yy1 = max(bi[1], bj[1])
+                xx2 = min(bi[2], bj[2])
+                yy2 = min(bi[3], bj[3])
+                inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1)
+                area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
+                area_j = (bj[2] - bj[0]) * (bj[3] - bj[1])
+                iou = inter / (area_i + area_j - inter + 1e-7)
+                if iou > iou_thresh:
+                    suppressed[jdx] = True
+        return np.array(keep, dtype=np.intp)
+
+    def _per_class_hard_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+        all_keep: list[int] = []
+        for c in np.unique(cls_ids):
+            mask = cls_ids == c
+            indices = np.where(mask)[0]
+            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
+            all_keep.extend(indices[keep].tolist())
+        all_keep.sort()
+        return np.array(all_keep, dtype=np.intp)
+
+    @staticmethod
+    def _cross_class_dedup(
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        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)
+        areas = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(
+            0.0, boxes[:, 3] - boxes[:, 1]
+        )
+        # Keep larger boxes first, then higher score.
+        order = np.lexsort((-scores, -areas))
+        suppressed = np.zeros(n, dtype=bool)
+        keep: list[int] = []
+        for i in order:
+            if suppressed[i]:
+                continue
+            keep.append(int(i))
+            bi = boxes[i]
+            xx1 = np.maximum(bi[0], boxes[:, 0])
+            yy1 = np.maximum(bi[1], boxes[:, 1])
+            xx2 = np.minimum(bi[2], boxes[:, 2])
+            yy2 = np.minimum(bi[3], boxes[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            area_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
+            union = area_i + areas - inter + 1e-7
+            iou = inter / union
+            dup = iou > iou_thresh
+            dup[i] = False
+            suppressed |= dup
+        keep_idx = np.array(keep, dtype=np.intp)
+        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
+
+    @staticmethod
+    def _max_score_per_cluster(
+        coords: np.ndarray,
+        scores: np.ndarray,
+        keep_indices: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        n_keep = len(keep_indices)
+        if n_keep == 0:
+            return np.array([], dtype=np.float32)
+        coords = np.asarray(coords, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        out = np.empty(n_keep, dtype=np.float32)
+        for i in range(n_keep):
+            idx = keep_indices[i]
+            bi = coords[idx]
+            xx1 = np.maximum(bi[0], coords[:, 0])
+            yy1 = np.maximum(bi[1], coords[:, 1])
+            xx2 = np.minimum(bi[2], coords[:, 2])
+            yy2 = np.minimum(bi[3], coords[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
+            areas_j = (coords[:, 2] - coords[:, 0]) * (coords[:, 3] - coords[:, 1])
+            iou = inter / (area_i + areas_j - inter + 1e-7)
+            in_cluster = iou >= iou_thresh
+            out[i] = float(np.max(scores[in_cluster]))
+        return out
+
+    def _decode_raw_dets(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Decode end2end NMS output and return (boxes, scores, cls_ids)
+        in original image coordinates, after conf-threshold + remap + letterbox-reverse + sanity."""
+        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 ONNX output shape: {preds.shape}")
+
+        boxes = preds[:, :4].astype(np.float32)
+        scores = preds[:, 4].astype(np.float32)
+        cls_ids = preds[:, 5].astype(np.int32)
+
+        valid = (cls_ids >= 0) & (cls_ids < len(self.cls_remap))
+        boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid]
+        cls_ids = self.cls_remap[cls_ids]
+
+        keep = scores >= self.conf_thres
+        boxes = boxes[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+        if len(boxes) == 0:
+            return (
+                np.empty((0, 4), dtype=np.float32),
+                np.empty((0,), dtype=np.float32),
+                np.empty((0,), dtype=np.int32),
+            )
+
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
+        return boxes, scores, cls_ids
+
+    def _forward(
+        self, image: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        x, ratio, pad, orig_size = self._preprocess(image)
+        out = self.session.run(self.output_names, {self.input_name: x})[0]
+        return self._decode_raw_dets(out, ratio, pad, orig_size)
+
+    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
+        boxes, scores, cls_ids = self._forward(image)
+        if len(boxes) == 0:
+            return []
+        return self._build_results(boxes, scores, cls_ids)
+
+    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
+        """Hflip TTA: merge primary + flipped via per-class hard-NMS,
+        then cross-class dedup, with consensus-confidence boost."""
+        ow = image.shape[1]
+        b1, s1, c1 = self._forward(image)
+
+        flipped = cv2.flip(image, 1)
+        b2, s2, c2 = self._forward(flipped)
+        if len(b2):
+            x1f = ow - b2[:, 2]
+            x2f = ow - b2[:, 0]
+            b2 = np.stack([x1f, b2[:, 1], x2f, b2[:, 3]], axis=1)
+
+        if len(b1) == 0 and len(b2) == 0:
+            return []
+
+        boxes = np.concatenate([b1, b2], axis=0) if len(b2) else b1
+        scores = np.concatenate([s1, s2], axis=0) if len(b2) else s1
+        cls_ids = np.concatenate([c1, c2], axis=0) if len(b2) else c1
+
+        keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
+        if len(keep) == 0:
+            return []
+        keep = keep[: self.max_det]
+
+        # Consensus-confidence boost: cluster by IoU and take max score.
+        boosted = self._max_score_per_cluster(boxes, scores, keep, self.iou_thres)
+
+        boxes = boxes[keep]
+        cls_ids = cls_ids[keep]
+        scores = boosted
+
+        boxes, scores, cls_ids = self._cross_class_dedup(
+            boxes, scores, cls_ids, self.cross_iou_thresh
+        )
+        if len(boxes) == 0:
+            return []
+
+        return self._build_results(boxes, scores, cls_ids)
+
+    def _build_results(
+        self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray
+    ) -> list[BoundingBox]:
+        results: list[BoundingBox] = []
+        for box, conf, cls_id in zip(boxes, scores, cls_ids):
+            x1, y1, x2, y2 = box.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=int(cls_id),
+                    conf=float(conf),
+                )
+            )
+        return results
+
+    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):
+            if image is None or not isinstance(image, np.ndarray) or image.ndim != 3:
+                results.append(
+                    TVFrameResult(
+                        frame_id=offset + frame_number_in_batch,
+                        boxes=[],
+                        keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
+                    )
+                )
+                continue
+            if image.dtype != np.uint8:
+                image = image.astype(np.uint8)
+            try:
+                if self.use_tta:
+                    boxes = self._predict_tta(image)
+                else:
+                    boxes = self._predict_single(image)
+            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)))],
+                )
+            )
+        return results

weights.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19ae76c63971dadbb0633df2bd60fbe4539a256d3a55471666b5362a664c5bbe
+size 19300186