Cureco学習プラットフォーム用ONNX推論エンジン
Project description
cureco-inference-core
Cureco学習プラットフォームでエクスポートしたONNXモデルを使用して、画像分類などの推論を実行するためのPythonパッケージです。
特徴
- ハードウェア自動検出: NPU/GPU/CPUを自動検出し、最適なExecution Providerを選択
- 前処理・後処理の自動化: モデルメタデータから正規化パラメータやクラスマッピングを自動読み取り
- 拡張可能な設計: 抽象基底クラスを継承して、独自の推論タイプを追加可能
インストール
pip install cureco-inference-core
GPU (CUDA) を使用する場合:
pip install cureco-inference-core[gpu]
DirectML (Windows GPU) を使用する場合:
pip install cureco-inference-core[directml]
クイックスタート
from PIL import Image
from cureco_inference_core import CurecoInference
engine = CurecoInference()
engine.load_model("model.onnx")
image = Image.open("test.jpg").convert("RGB")
results = engine.inference(image, params=[1.0])
for result in results:
print(f"{result['label']}: {result['confidence']:.2%}")
メタデータの軽量読み取り
推論セッションを起動せずに、モデルのクラス名だけを取得できます。
classes = CurecoInference.read_classes("model.onnx")
print(classes) # ['cat', 'dog', 'bird']
対応 Execution Provider
優先順位順:
| Provider | ハードウェア |
|---|---|
| QNNExecutionProvider | Qualcomm NPU |
| VitisAIExecutionProvider | AMD Ryzen AI |
| OpenVINOExecutionProvider | Intel NPU |
| CUDAExecutionProvider | NVIDIA GPU |
| DmlExecutionProvider | DirectML (Windows GPU) |
| RKNPUExecutionProvider | Rockchip NPU |
| CPUExecutionProvider | CPU (フォールバック) |
ライセンス
MIT
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