scorevision: push artifact

miner.py

@@ -1,339 +1,175 @@
-"""
-Score Vision SN44 — VehicleDetect miner endpoint.
-
-Class mapping (output indices):
-  0 = car        (COCO class 2)
-  1 = bus        (COCO class 5)
-  2 = truck      (COCO class 7)
-  3 = motorcycle (COCO class 3)
-
-Accepts: base64-encoded image or raw image bytes via chutes cord.
-Returns: list of {bbox: [x1,y1,x2,y2], score: float, class_id: int, class_name: str}
-
-CUDA fix: onnxruntime-gpu finds cuDNN via ldconfig (registered during image build),
-          with ctypes preload as belt-and-suspenders fallback.
-"""
-
-from __future__ import annotations
-
-import base64
-import io
-import os
-import time
 from pathlib import Path
-from typing import Any
+import math
 
-import ctypes
 import cv2
 import numpy as np
-from PIL import Image
-
-# ── cuDNN preload (belt-and-suspenders fallback) ──────────────────────────────
-# Primary fix is ldconfig at image build time (see Image builder below).
-# This ctypes preload catches any edge cases where ld.so.cache isn't used.
-def _preload_cuda_libs() -> None:
-    _NVIDIA = "/usr/local/lib/python3.12/dist-packages/nvidia"
-    _LIBS = [
-        "/usr/lib/x86_64-linux-gnu/libcuda.so.1",         # driver stub — must be first
-        f"{_NVIDIA}/cublas/lib/libcublasLt.so.12",
-        f"{_NVIDIA}/cublas/lib/libcublas.so.12",
-        f"{_NVIDIA}/cudnn/lib/libcudnn.so.9",
-    ]
-    for path in _LIBS:
-        if os.path.exists(path):
-            try:
-                ctypes.CDLL(path, mode=ctypes.RTLD_GLOBAL)
-            except OSError:
-                pass
-
-_preload_cuda_libs()
-
-import onnxruntime as ort  # noqa: E402 — must come after preload
-
-# ── Constants ────────────────────────────────────────────────────────────────
-MODEL_DIR   = Path(__file__).parent
-WEIGHTS     = MODEL_DIR / "weights.onnx"
-IMG_SIZE    = 640
-CONF_THRESH = 0.55  # sweep: max composite score (0.60×mAP + 0.40×FP_score) at conf=0.55
-IOU_THRESH  = 0.45
-
-# COCO class index → submission class index
-COCO_TO_OUT: dict[int, int] = {2: 0, 5: 1, 7: 2, 3: 3}
-COCO_VEHICLE_IDX = list(COCO_TO_OUT.keys())
-OUT_NAMES = ["car", "bus", "truck", "motorcycle"]
-
-# ── Model loader (singleton) ─────────────────────────────────────────────────
-_SESSION: ort.InferenceSession | None = None
-
-
-def get_session() -> ort.InferenceSession:
-    global _SESSION
-    if _SESSION is None:
-        opts = ort.SessionOptions()
-        opts.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-        opts.enable_mem_pattern = True
-        opts.enable_mem_reuse   = True
-        cuda_opts = {
-            "device_id": 0,
-            "arena_extend_strategy": "kNextPowerOfTwo",
-            "gpu_mem_limit": 2 * 1024 ** 3,
-            "cudnn_conv_algo_search": "EXHAUSTIVE",
-            "do_copy_in_default_stream": True,
-        }
-        _SESSION = ort.InferenceSession(
-            str(WEIGHTS),
-            sess_options=opts,
-            providers=[
-                ("CUDAExecutionProvider", cuda_opts),
-                "CPUExecutionProvider",
-            ],
-        )
-        provider = _SESSION.get_providers()[0]
-        print(f"[miner] Model loaded. Provider: {provider}", flush=True)
-    return _SESSION
-
-
-# ── Preprocessing ────────────────────────────────────────────────────────────
-
-def letterbox(img: np.ndarray, size: int = IMG_SIZE) -> tuple[np.ndarray, float, int, int]:
-    h, w = img.shape[:2]
-    r = min(size / h, size / w)
-    new_w, new_h = int(round(w * r)), int(round(h * r))
-    img_r = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
-    dw, dh = size - new_w, size - new_h
-    pad_l, pad_t = dw // 2, dh // 2
-    img_p = cv2.copyMakeBorder(
-        img_r, pad_t, dh - pad_t, pad_l, dw - pad_l,
-        cv2.BORDER_CONSTANT, value=(114, 114, 114),
-    )
-    return img_p, r, pad_l, pad_t
-
-
-def preprocess(img_bgr: np.ndarray) -> tuple[np.ndarray, float, int, int]:
-    img_p, ratio, pad_l, pad_t = letterbox(img_bgr)
-    img_rgb = cv2.cvtColor(img_p, cv2.COLOR_BGR2RGB)
-    inp = img_rgb.transpose(2, 0, 1).astype(np.float32) * (1.0 / 255.0)
-    return np.ascontiguousarray(inp[np.newaxis]), ratio, pad_l, pad_t
-
-
-# ── NMS ──────────────────────────────────────────────────────────────────────
-
-def nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float = IOU_THRESH) -> list[int]:
-    if not len(boxes):
-        return []
-    x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
-    areas = (x2 - x1) * (y2 - y1)
-    order = scores.argsort()[::-1]
-    keep: list[int] = []
-    while len(order):
-        i = order[0]
-        keep.append(int(i))
-        xx1 = np.maximum(x1[i], x1[order[1:]])
-        yy1 = np.maximum(y1[i], y1[order[1:]])
-        xx2 = np.minimum(x2[i], x2[order[1:]])
-        yy2 = np.minimum(y2[i], y2[order[1:]])
-        inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
-        iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-7)
-        order = order[1:][iou <= iou_thresh]
-    return keep
-
-
-# ── Postprocessing ───────────────────────────────────────────────────────────
-
-def postprocess(
-    raw: np.ndarray,
-    ratio: float,
-    pad_l: int,
-    pad_t: int,
-    orig_w: int,
-    orig_h: int,
-) -> list[dict[str, Any]]:
-    pred = raw  # [84, 8400]
-    veh_row_idx = np.array([4 + c for c in COCO_VEHICLE_IDX])
-    max_veh_score = pred[veh_row_idx].max(axis=0)
-    mask = max_veh_score > CONF_THRESH
-    if not mask.any():
-        return []
-
-    pred_f = pred[:, mask]
-    cx, cy, bw, bh = pred_f[0], pred_f[1], pred_f[2], pred_f[3]
-
-    x1 = np.clip((cx - bw / 2 - pad_l) / ratio, 0, orig_w)
-    y1 = np.clip((cy - bh / 2 - pad_t) / ratio, 0, orig_h)
-    x2 = np.clip((cx + bw / 2 - pad_l) / ratio, 0, orig_w)
-    y2 = np.clip((cy + bh / 2 - pad_t) / ratio, 0, orig_h)
-    boxes = np.stack([x1, y1, x2, y2], axis=1)
-
-    results: list[dict[str, Any]] = []
-    for coco_cls in COCO_VEHICLE_IDX:
-        scores = pred_f[4 + coco_cls]
-        cls_mask = scores > CONF_THRESH
-        if not cls_mask.any():
-            continue
-        keep = nms(boxes[cls_mask], scores[cls_mask])
-        out_cls = COCO_TO_OUT[coco_cls]
-        for k in keep:
-            box = boxes[cls_mask][k]
-            results.append({
-                "bbox": [
-                    float(box[0]), float(box[1]),
-                    float(box[2]), float(box[3]),
-                ],
-                "score": float(scores[cls_mask][k]),
-                "class_id": out_cls,
-                "class_name": OUT_NAMES[out_cls],
-            })
-    return results
-
-
-# ── Image decoding helpers ───────────────────────────────────────────────────
-
-def decode_image(data: bytes | str) -> np.ndarray:
-    if isinstance(data, str):
-        data = base64.b64decode(data)
-    elif isinstance(data, (bytes, bytearray)):
-        try:
-            data = base64.b64decode(data)
-        except Exception:
-            pass
-    arr = np.frombuffer(data, dtype=np.uint8)
-    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
-    if img is None:
-        pil = Image.open(io.BytesIO(data)).convert("RGB")
-        img = cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
-    return img
-
-
-# ── Core predict function ────────────────────────────────────────────────────
-
-def predict(image_data: bytes | str | np.ndarray) -> dict[str, Any]:
-    sess = get_session()
-
-    if isinstance(image_data, np.ndarray):
-        img_bgr = image_data
-    else:
-        img_bgr = decode_image(image_data)
-
-    orig_h, orig_w = img_bgr.shape[:2]
-    inp, ratio, pad_l, pad_t = preprocess(img_bgr)
-
-    t0 = time.perf_counter()
-    outputs = sess.run(None, {"images": inp})
-    infer_ms = (time.perf_counter() - t0) * 1000.0
-
-    raw = outputs[0][0]   # [84, 8400]
-    detections = postprocess(raw, ratio, pad_l, pad_t, orig_w, orig_h)
-
-    return {
-        "detections": detections,
-        "inference_ms": round(infer_ms, 3),
-        "provider": sess.get_providers()[0],
-    }
-
-
-# ── Chutes cord wrapper ──────────────────────────────────────────────────────
-
-try:
-    from chutes.chute import Chute
-    from chutes.chute.node_selector import NodeSelector
-    from chutes.image import Image as ChuteImage
-
-    chute_image = (
-        ChuteImage(
-            username="lculpitt",
-            name="vehicle-detect-sn44",
-            tag="v4-cuda",
-            readme=(Path(__file__).parent / "README.md").read_text(),
-        )
-        .from_base("parachutes/python:3.12")
-        .run_command("pip install --upgrade setuptools wheel")
-        .run_command(
-            "pip install 'numpy>=1.23' 'onnxruntime-gpu>=1.16' "
-            "'opencv-python-headless>=4.7' 'pillow>=9.5' "
-            "'huggingface_hub>=0.19.4' 'pydantic>=2.0' "
-            "'pyyaml>=6.0' 'aiohttp>=3.9'"
+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:
+    """
+    Auto-generated by subnet_bridge from a Manako element repo.
+    This miner is intentionally self-contained for chute import restrictions.
+    """
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        self.path_hf_repo = path_hf_repo
+        self.class_names = ['person']
+        self.session = ort.InferenceSession(
+            str(path_hf_repo / "weights.onnx"),
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
         )
-        # Bake cuDNN/cuBLAS paths into the image as Docker ENV so onnxruntime
-        # CUDAExecutionProvider finds libcudnn.so.9 on every node at container start.
-        .with_env(
-            "LD_LIBRARY_PATH",
-            "/usr/local/lib/python3.12/dist-packages/nvidia/cudnn/lib"
-            ":/usr/local/lib/python3.12/dist-packages/nvidia/cublas/lib",
-        )
-    )
-
-    chute = Chute(
-        username="lculpitt",
-        name="vehicle-detect-sn44",
-        tagline="YOLO11n vehicle detector — car, bus, truck, motorcycle",
-        readme=(Path(__file__).parent / "README.md").read_text(),
-        image=chute_image,
-        concurrency=4,
-        max_instances=5,
-        shutdown_after_seconds=300,
-        scaling_threshold=0.5,
-        node_selector=NodeSelector(
-            gpu_count=1,
-            min_vram_gb_per_gpu=16,
-            # All CUDA 12.x, all $0.40–$0.85/hr (within 2.5× spread from cheapest)
-            include=["4090", "a40", "a6000", "l40", "l40s"],
-        ),
-    )
-
-    @chute.cord(path="/predict", method="POST")
-    async def predict_cord(image_b64: str) -> dict:
-        """
-        POST /predict
-        Body: {"image_b64": "<base64-encoded image>"}
-        Returns detection JSON.
-        """
-        return predict(image_b64)
-
-except ImportError:
-    pass
-
-
-# ── Local test ───────────────────────────────────────────────────────────────
-
-if __name__ == "__main__":
-    import sys
-
-    print("=" * 55)
-    print("  miner.py — local smoke test")
-    print("=" * 55)
-
-    dummy_bgr = np.full((720, 1280, 3), 128, dtype=np.uint8)
-    cv2.rectangle(dummy_bgr, (100, 100), (400, 300), (0, 255, 0), 3)
-
-    if len(sys.argv) > 1:
-        loaded = cv2.imread(sys.argv[1])
-        if loaded is not None:
-            dummy_bgr = loaded
-            print(f"  Using image: {sys.argv[1]}  ({loaded.shape[1]}x{loaded.shape[0]})")
-        else:
-            print(f"  Could not load {sys.argv[1]}, using dummy.")
-    else:
-        print("  Using synthetic 1280x720 dummy image.")
-
-    result = predict(dummy_bgr)
-    print(f"\n  Provider     : {result['provider']}")
-    print(f"  Inference    : {result['inference_ms']:.2f} ms")
-    print(f"  Detections   : {len(result['detections'])}")
-    for d in result["detections"]:
-        x1, y1, x2, y2 = [round(v, 1) for v in d["bbox"]]
-        print(f"    [{d['class_id']}] {d['class_name']:12s}  score={d['score']:.3f}  "
-              f"bbox=[{x1},{y1},{x2},{y2}]")
-
-    print("\n  Latency benchmark (50 runs)...")
-    times = []
-    for _ in range(50):
-        t0 = time.perf_counter()
-        predict(dummy_bgr)
-        times.append((time.perf_counter() - t0) * 1000)
-    times.sort()
-    p50, p95 = times[25], times[47]
-    fps = 1000.0 / p50
-    print(f"  P50={p50:.2f}ms  P95={p95:.2f}ms  FPS={fps:.1f}")
-    print(f"  Target >=30 FPS  : {'PASS' if fps >= 30 else 'FAIL'}")
-    print(f"  Target P95<50ms  : {'PASS' if p95 < 50 else 'FAIL'}")
-    print("=" * 55)
+        self.input_name = self.session.get_inputs()[0].name
+        input_shape = self.session.get_inputs()[0].shape
+        # expected [N, C, H, W]
+        self.input_h = int(input_shape[2])
+        self.input_w = int(input_shape[3])
+        self.conf_threshold = 0.70  # sweep-optimised: max composite 0.65×mAP+0.35×FP_score
+        self.iou_threshold = 0.45
+
+    def __repr__(self) -> str:
+        return f"ONNX Miner session={type(self.session).__name__} classes={len(self.class_names)}"
+
+    def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, tuple[int, int]]:
+        h, w = image_bgr.shape[:2]
+        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+        resized = cv2.resize(rgb, (self.input_w, self.input_h))
+        x = resized.astype(np.float32) / 255.0
+        x = np.transpose(x, (2, 0, 1))[None, ...]
+        return x, (h, w)
+
+    def _normalize_predictions(self, raw: np.ndarray) -> np.ndarray:
+        # Common ultralytics export shapes:
+        # - [1, C, N] where C=4+num_classes
+        # - [1, N, C]
+        pred = raw[0]
+        if pred.ndim != 2:
+            raise ValueError(f"Unexpected prediction shape: {raw.shape}")
+        if pred.shape[0] < pred.shape[1]:
+            pred = pred.transpose(1, 0)
+        return pred
+
+    def _nms(self, dets: list[tuple[float, float, float, float, float, int]]) -> list[tuple[float, float, float, float, float, int]]:
+        if not dets:
+            return []
+
+        boxes = np.array([[d[0], d[1], d[2], d[3]] for d in dets], dtype=np.float32)
+        scores = np.array([d[4] for d in dets], dtype=np.float32)
+        order = scores.argsort()[::-1]
+        keep = []
+
+        while order.size > 0:
+            i = order[0]
+            keep.append(i)
+
+            xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])
+
+            w = np.maximum(0.0, xx2 - xx1)
+            h = np.maximum(0.0, yy2 - yy1)
+            inter = w * h
+
+            area_i = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            area_rest = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1])
+            union = np.maximum(area_i + area_rest - inter, 1e-6)
+            iou = inter / union
+
+            remaining = np.where(iou <= self.iou_threshold)[0]
+            order = order[remaining + 1]
+
+        return [dets[idx] for idx in keep]
+
+    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
+        inp, (orig_h, orig_w) = self._preprocess(image_bgr)
+        out = self.session.run(None, {self.input_name: inp})[0]
+        pred = self._normalize_predictions(out)
+
+        if pred.shape[1] < 5:
+            return []
+
+        boxes = pred[:, :4]
+        cls_scores = pred[:, 4:]
+
+        if cls_scores.shape[1] == 0:
+            return []
+
+        cls_ids = np.argmax(cls_scores, axis=1)
+        confs = np.max(cls_scores, axis=1)
+        keep = confs >= self.conf_threshold
+
+        boxes = boxes[keep]
+        confs = confs[keep]
+        cls_ids = cls_ids[keep]
+
+        if boxes.shape[0] == 0:
+            return []
+
+        sx = orig_w / float(self.input_w)
+        sy = orig_h / float(self.input_h)
+
+        dets: list[tuple[float, float, float, float, float, int]] = []
+        for i in range(boxes.shape[0]):
+            cx, cy, bw, bh = boxes[i].tolist()
+            x1 = (cx - bw / 2.0) * sx
+            y1 = (cy - bh / 2.0) * sy
+            x2 = (cx + bw / 2.0) * sx
+            y2 = (cy + bh / 2.0) * sy
+            dets.append((x1, y1, x2, y2, float(confs[i]), int(cls_ids[i])))
+
+        dets = self._nms(dets)
+
+        out_boxes: list[BoundingBox] = []
+        for x1, y1, x2, y2, conf, cls_id in dets:
+            ix1 = max(0, min(orig_w, math.floor(x1)))
+            iy1 = max(0, min(orig_h, math.floor(y1)))
+            ix2 = max(0, min(orig_w, math.ceil(x2)))
+            iy2 = max(0, min(orig_h, math.ceil(y2)))
+            out_boxes.append(
+                BoundingBox(
+                    x1=ix1,
+                    y1=iy1,
+                    x2=ix2,
+                    y2=iy2,
+                    cls_id=cls_id,
+                    conf=max(0.0, min(1.0, conf)),
+                )
+            )
+        return out_boxes
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        results: list[TVFrameResult] = []
+        for idx, image in enumerate(batch_images):
+            boxes = self._infer_single(image)
+            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + idx,
+                    boxes=boxes,
+                    keypoints=keypoints,
+                )
+            )
+        return results