chore: 添加虚拟环境到仓库
- 添加 backend_service/venv 虚拟环境 - 包含所有Python依赖包 - 注意：虚拟环境约393MB，包含12655个文件
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------------------
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"""
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Implements ONNX's backend API.
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"""
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import os
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import unittest
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import packaging.version
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from onnx import ModelProto, helper, version # noqa: F401
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from onnx.backend.base import Backend
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from onnx.checker import check_model
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from onnxruntime import InferenceSession, SessionOptions, get_available_providers, get_device
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from onnxruntime.backend.backend_rep import OnnxRuntimeBackendRep
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class OnnxRuntimeBackend(Backend):
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"""
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Implements
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`ONNX's backend API <https://github.com/onnx/onnx/blob/main/docs/ImplementingAnOnnxBackend.md>`_
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with *ONNX Runtime*.
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The backend is mostly used when you need to switch between
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multiple runtimes with the same API.
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`Importing models from ONNX to Caffe2 <https://github.com/onnx/tutorials/blob/master/tutorials/OnnxCaffe2Import.ipynb>`_
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shows how to use *caffe2* as a backend for a converted model.
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Note: This is not the official Python API.
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"""
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allowReleasedOpsetsOnly = bool(os.getenv("ALLOW_RELEASED_ONNX_OPSET_ONLY", "1") == "1") # noqa: N815
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@classmethod
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def is_compatible(cls, model, device=None, **kwargs):
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"""
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Return whether the model is compatible with the backend.
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:param model: unused
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:param device: None to use the default device or a string (ex: `'CPU'`)
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:return: boolean
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"""
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if device is None:
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device = get_device()
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return cls.supports_device(device)
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@classmethod
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def is_opset_supported(cls, model):
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"""
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Return whether the opset for the model is supported by the backend.
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When By default only released onnx opsets are allowed by the backend
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To test new opsets env variable ALLOW_RELEASED_ONNX_OPSET_ONLY should be set to 0
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:param model: Model whose opsets needed to be verified.
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:return: boolean and error message if opset is not supported.
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"""
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if cls.allowReleasedOpsetsOnly:
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for opset in model.opset_import:
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domain = opset.domain if opset.domain else "ai.onnx"
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try:
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key = (domain, opset.version)
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if key not in helper.OP_SET_ID_VERSION_MAP:
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error_message = (
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"Skipping this test as only released onnx opsets are supported."
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"To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY to 0."
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f" Got Domain '{domain}' version '{opset.version}'."
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)
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return False, error_message
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except AttributeError:
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# for some CI pipelines accessing helper.OP_SET_ID_VERSION_MAP
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# is generating attribute error. TODO investigate the pipelines to
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# fix this error. Falling back to a simple version check when this error is encountered
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if (domain == "ai.onnx" and opset.version > 12) or (domain == "ai.ommx.ml" and opset.version > 2):
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error_message = (
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"Skipping this test as only released onnx opsets are supported."
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"To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY to 0."
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f" Got Domain '{domain}' version '{opset.version}'."
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)
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return False, error_message
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return True, ""
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@classmethod
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def supports_device(cls, device):
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"""
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Check whether the backend is compiled with particular device support.
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In particular it's used in the testing suite.
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"""
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if device == "CUDA":
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device = "GPU"
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return "-" + device in get_device() or device + "-" in get_device() or device == get_device()
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@classmethod
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def prepare(cls, model, device=None, **kwargs):
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"""
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Load the model and creates a :class:`onnxruntime.InferenceSession`
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ready to be used as a backend.
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:param model: ModelProto (returned by `onnx.load`),
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string for a filename or bytes for a serialized model
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:param device: requested device for the computation,
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None means the default one which depends on
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the compilation settings
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:param kwargs: see :class:`onnxruntime.SessionOptions`
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:return: :class:`onnxruntime.InferenceSession`
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"""
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if isinstance(model, OnnxRuntimeBackendRep):
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return model
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elif isinstance(model, InferenceSession):
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return OnnxRuntimeBackendRep(model)
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elif isinstance(model, (str, bytes)):
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options = SessionOptions()
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for k, v in kwargs.items():
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if hasattr(options, k):
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setattr(options, k, v)
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excluded_providers = os.getenv("ORT_ONNX_BACKEND_EXCLUDE_PROVIDERS", default="").split(",")
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providers = [x for x in get_available_providers() if (x not in excluded_providers)]
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inf = InferenceSession(model, sess_options=options, providers=providers)
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# backend API is primarily used for ONNX test/validation. As such, we should disable session.run() fallback
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# which may hide test failures.
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inf.disable_fallback()
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if device is not None and not cls.supports_device(device):
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raise RuntimeError(f"Incompatible device expected '{device}', got '{get_device()}'")
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return cls.prepare(inf, device, **kwargs)
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else:
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# type: ModelProto
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# check_model serializes the model anyways, so serialize the model once here
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# and reuse it below in the cls.prepare call to avoid an additional serialization
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# only works with onnx >= 1.10.0 hence the version check
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onnx_version = packaging.version.parse(version.version) or packaging.version.Version("0")
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onnx_supports_serialized_model_check = onnx_version.release >= (1, 10, 0)
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bin_or_model = model.SerializeToString() if onnx_supports_serialized_model_check else model
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check_model(bin_or_model)
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opset_supported, error_message = cls.is_opset_supported(model)
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if not opset_supported:
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raise unittest.SkipTest(error_message)
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# Now bin might be serialized, if it's not we need to serialize it otherwise we'll have
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# an infinite recursive call
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bin = bin_or_model
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if not isinstance(bin, (str, bytes)):
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bin = bin.SerializeToString()
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return cls.prepare(bin, device, **kwargs)
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@classmethod
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def run_model(cls, model, inputs, device=None, **kwargs):
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"""
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Compute the prediction.
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:param model: :class:`onnxruntime.InferenceSession` returned
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by function *prepare*
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:param inputs: inputs
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:param device: requested device for the computation,
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None means the default one which depends on
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the compilation settings
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:param kwargs: see :class:`onnxruntime.RunOptions`
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:return: predictions
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"""
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rep = cls.prepare(model, device, **kwargs)
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return rep.run(inputs, **kwargs)
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@classmethod
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def run_node(cls, node, inputs, device=None, outputs_info=None, **kwargs):
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"""
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This method is not implemented as it is much more efficient
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to run a whole model than every node independently.
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"""
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raise NotImplementedError("It is much more efficient to run a whole model than every node independently.")
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is_compatible = OnnxRuntimeBackend.is_compatible
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prepare = OnnxRuntimeBackend.prepare
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run = OnnxRuntimeBackend.run_model
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supports_device = OnnxRuntimeBackend.supports_device
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