Upload monochrome_detection/onnxruntime_.py with huggingface_hub

monochrome_detection/onnxruntime_.py

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+import logging
+import os
+import shutil
+from typing import Optional
+
+from hbutils.system import pip_install
+
+__all__ = [
+    'get_onnx_provider', 'open_onnx_model'
+]
+
+
+def _ensure_onnxruntime():
+    try:
+        import onnxruntime
+    except (ImportError, ModuleNotFoundError):
+        logging.warning('Onnx runtime not installed, preparing to install ...')
+        if shutil.which('nvidia-smi'):
+            logging.info('Installing onnxruntime-gpu ...')
+            pip_install(['onnxruntime-gpu'], silent=True)
+        else:
+            logging.info('Installing onnxruntime (cpu) ...')
+            pip_install(['onnxruntime'], silent=True)
+
+
+_ensure_onnxruntime()
+from onnxruntime import get_available_providers, get_all_providers, InferenceSession, SessionOptions, \
+    GraphOptimizationLevel
+
+alias = {
+    'gpu': "CUDAExecutionProvider",
+    "trt": "TensorrtExecutionProvider",
+}
+
+
+def get_onnx_provider(provider: Optional[str] = None):
+    if not provider:
+        if "CUDAExecutionProvider" in get_available_providers():
+            return "CUDAExecutionProvider"
+        else:
+            return "CPUExecutionProvider"
+    elif provider.lower() in alias:
+        return alias[provider.lower()]
+    else:
+        for p in get_all_providers():
+            if provider.lower() == p.lower() or f'{provider}ExecutionProvider'.lower() == p.lower():
+                return p
+
+        raise ValueError(f'One of the {get_all_providers()!r} expected, '
+                         f'but unsupported provider {provider!r} found.')
+
+
+def _open_onnx_model(ckpt: str, provider: str) -> InferenceSession:
+    options = SessionOptions()
+    options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
+    if provider == "CPUExecutionProvider":
+        options.intra_op_num_threads = os.cpu_count()
+
+    logging.info(f'Model {ckpt!r} loaded with provider {provider!r}')
+    return InferenceSession(ckpt, options, [provider])
+
+
+def open_onnx_model(ckpt: str, mode: str = None) -> InferenceSession:
+    return _open_onnx_model(ckpt, get_onnx_provider(mode))