Add model files and configurations with LFS

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.so filter=lfs diff=lfs merge=lfs -text
+models/osnet_model.pth.tar-100 filter=lfs diff=lfs merge=lfs -text
+keypoint filter=lfs diff=lfs merge=lfs -text
+osnet_model.pth.tar-100 filter=lfs diff=lfs merge=lfs -text

chute_config.yml

@@ -0,0 +1,28 @@
+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install --index-url https://download.pytorch.org/whl/cu128 torch torchvision
+    - pip install "ultralytics==8.3.222" "opencv-python-headless" "numpy" "pydantic" "Pillow"
+    - pip install scikit-learn
+    - pip install onnxruntime-gpu
+    - pip install lapx
+  set_workdir: /app
+  readme: "Image for chutes"
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 24
+  min_cpu_count: 8
+  min_memory_gb: 16
+  exclude:
+    - b200
+    - h200
+    - mi300x
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 96000

keypoints_convert.cpython-312-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
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+oid sha256:57dc1ff1747bb9394a64ae1d3ac36be297e18ca8bf345979b09710e1d522397a
+size 22452368

keypoints_cy_all.cpython-312-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:556f305991967aa60fb04458e789dc793d855765ae52a207ecf16ff8dada9f0c
+size 1422656

miner.py

@@ -0,0 +1,100 @@
+import os
+import sys
+from pathlib import Path
+from typing import List, Tuple
+
+from numpy import ndarray
+from pydantic import BaseModel
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+# from inference import predict_batch, load_model
+import importlib.util
+from pathlib import Path
+
+def manual_import(name, filename):
+    curr_dir = Path(__file__).parent
+    file_path = curr_dir / filename
+    
+    if not file_path.exists():
+        raise FileNotFoundError(f"Could not find {file_path}")
+
+    spec = importlib.util.spec_from_file_location(name, file_path)
+    if spec is None:
+        raise ImportError(f"Could not load spec for {name} from {file_path}")
+        
+    module = importlib.util.module_from_spec(spec)
+    # No sys.modules registration needed - pipeline.py uses regular classes, not dataclasses
+    spec.loader.exec_module(module)
+    return module
+
+
+def manual_import_pipeline(name: str):
+    """
+    Manual import for pipeline module, bypassing sys.meta_path.
+    Tries .py first (development), then .so (production).
+    """
+    curr_dir = Path(__file__).parent
+    last_error = None
+    
+    # Try .py first (development)
+    py_path = curr_dir / f"{name}.py"
+    if py_path.is_file():
+        try:
+            return manual_import(name, f"{name}.py")
+        except Exception as e:
+            last_error = e  # Save error, try .so next
+    
+    # Try .so files (production)
+    so_candidates = sorted(curr_dir.glob(f"{name}*.so"))
+    if so_candidates:
+        so_filename = so_candidates[0].name
+        try:
+            return manual_import(name, so_filename)
+        except Exception as e:
+            last_error = e
+    
+    # If we have a saved error, raise it with context
+    if last_error:
+        raise ImportError(f"Could not import {name} from {curr_dir}: {last_error}") from last_error
+    raise ImportError(f"Could not find {name}.py or {name}*.so in {curr_dir}")
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+    team_id: str | None = None
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: List[BoundingBox]
+    keypoints: List[Tuple[float, float]]
+
+
+class Miner:
+    def __init__(self, path_hf_repo: Path) -> None:
+        print("model laoding")
+        self.health = 'Okay-turbo-16'
+        self.pipeline = None
+        self.path_hf_repo = path_hf_repo
+        self.is_start = False
+
+    def __repr__(self) -> str:
+        return self.health
+
+    def predict_batch(self, batch_images: List[ndarray], offset: int, n_keypoints: int) -> List[TVFrameResult]:
+        if self.is_start == False:
+            self.is_start = True
+            return []
+        if self.pipeline is None:
+            pipeline_module = manual_import_pipeline("pipeline")
+            self.pipeline = pipeline_module.MinerPipeline(repo_root=self.path_hf_repo)
+        results = self.pipeline.predict_batch(
+            batch_images,
+            offset,
+            n_keypoints,
+        )
+        return results

models/ball-detection-model.onnx

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+size 10418757

models/keypoint-detection-model.onnx

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+size 10422736

models/osnet_model.pth.tar-100

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+size 36189570

models/person-detection-model.onnx

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+oid sha256:305fb39579bb3473db25acce3dd4a3944c63a3994eac573e46c6a3c372098e34
+size 10420354

pipeline.cpython-312-x86_64-linux-gnu.so

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+size 3720144