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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
+  set_workdir: /app
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 16
+  max_hourly_price_per_gpu: 1
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 288000

miner.py

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+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]]
+
+
+SIZE = 1280
+
+
+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():
+            lines = cn_path.read_text(encoding="utf-8").splitlines()
+            self.class_names = [
+                ln.strip()
+                for ln in lines
+                if ln.strip() and not ln.strip().startswith("#")
+            ]
+        else:
+            self.class_names = ["numberplate"]
+        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())
+
+        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_mem / 1e9:.1f} GB")
+            else:
+                print("GPU: CUDA not available via torch")
+        except Exception as e:
+            print(f"GPU detection failed: {e}")
+
+        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())
+
+        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
+
+        self.input_height = self._safe_dim(self.input_shape[2], default=SIZE)
+        self.input_width = self._safe_dim(self.input_shape[3], default=SIZE)
+
+        self.conf_thres = 0.35
+        self.iou_thres = 0.48
+        self.sigma = 0.5
+        self.max_det = 300
+
+        self.sparse_threshold = 3       # fire tiles only if primary returns < this
+        self.tile_conf = 0.39
+        self.tile_overlap = 0.12
+        self.novelty_iou = 0.18
+        self.final_max_det = 16
+        self.tile_use_hflip = False     # skip hflip tile pass to save ~4 forwards
+
+        self.use_tta = True
+
+        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(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
+
+    # ---------- 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)
+        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):
+        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=np.float32), ratio, pad
+
+    @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
+
+    # ---------- NMS primitives ----------
+    @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)
+        keep: list[int] = []
+        while len(order):
+            i = int(order[0])
+            keep.append(i)
+            if len(order) == 1:
+                break
+            rest = order[1:]
+            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
+            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)
+            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 _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:
+            out = out[0]
+        if out.shape[1] < 5:
+            return np.zeros((0, 5), dtype=np.float32)
+        boxes = out[:, :4].astype(np.float32)
+        scores = out[:, 4].astype(np.float32)
+        keep = scores >= conf
+        boxes, scores = boxes[keep], scores[keep]
+        if len(boxes) == 0:
+            return np.zeros((0, 5), dtype=np.float32)
+        boxes[:, [0, 2]] -= dw
+        boxes[:, [1, 3]] -= dh
+        boxes /= ratio
+        oh, ow = image.shape[:2]
+        boxes = self._clip_boxes(boxes, (ow, oh))
+        return np.concatenate([boxes, scores[:, None]], axis=1)
+
+    # ---------- 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)
+        d2 = self._raw_dets(flipped, self.conf_thres)
+        if len(d2):
+            w = image.shape[1]
+            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)
+        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]:
+        if image is None or not isinstance(image, np.ndarray) or image.ndim != 3:
+            return []
+        if image.shape[0] <= 0 or image.shape[1] <= 0 or image.shape[2] != 3:
+            return []
+        if image.dtype != np.uint8:
+            image = image.astype(np.uint8)
+
+        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),
+                )
+            )
+        return results
+
+    # ---------- chute entrypoint ----------
+    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:
+                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 sha256:6cbecfb7d55f2a337d173b87ee2c92770b8e041efd4b5f700e59766ebe2af2a5
+size 19405546