Source code for onnxruntime.capi.session

#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#--------------------------------------------------------------------------

import sys
import os

from onnxruntime.capi import _pybind_state as C


[docs]class InferenceSession: """ This is the main class used to run a model. """ def __init__(self, path_or_bytes, sess_options=None): """ :param path_or_bytes: filename or serialized model in a byte string :param sess_options: session options """ self._path_or_bytes = path_or_bytes self._sess_options = sess_options self._load_model() self._enable_fallback = True def _load_model(self, providers=[]): if self._sess_options: self._sess = C.InferenceSession( self._sess_options, C.get_session_initializer()) else: self._sess = C.InferenceSession( C.get_session_initializer(), C.get_session_initializer()) if isinstance(self._path_or_bytes, str): self._sess.load_model(self._path_or_bytes, providers) elif isinstance(self._path_or_bytes, bytes): self._sess.read_bytes(self._path_or_bytes, providers) elif isinstance(self._path_or_bytes, tuple): # to remove, hidden trick self._sess.load_model_no_init(self._path_or_bytes[0], providers) else: raise TypeError("Unable to load from type '{0}'".format(type(self._path_or_bytes))) self._inputs_meta = self._sess.inputs_meta self._outputs_meta = self._sess.outputs_meta self._overridable_initializers = self._sess.overridable_initializers self._model_meta = self._sess.model_meta self._providers = self._sess.get_providers() # Tensorrt can fall back to CUDA. All others fall back to CPU. if 'TensorrtExecutionProvider' in C.get_available_providers(): self._fallback_providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] else: self._fallback_providers = ['CPUExecutionProvider'] def _reset_session(self): "release underlying session object." # meta data references session internal structures # so they must be set to None to decrement _sess reference count. self._inputs_meta = None self._outputs_meta = None self._overridable_initializers = None self._model_meta = None self._providers = None self._sess = None
[docs] def get_inputs(self): "Return the inputs metadata as a list of :class:`onnxruntime.NodeArg`." return self._inputs_meta
[docs] def get_outputs(self): "Return the outputs metadata as a list of :class:`onnxruntime.NodeArg`." return self._outputs_meta
[docs] def get_overridable_initializers(self): "Return the inputs (including initializers) metadata as a list of :class:`onnxruntime.NodeArg`." return self._overridable_initializers
[docs] def get_modelmeta(self): "Return the metadata. See :class:`onnxruntime.ModelMetadata`." return self._model_meta
[docs] def get_providers(self): "Return list of registered execution providers." return self._providers
[docs] def set_providers(self, providers): """ Register the input list of execution providers. The underlying session is re-created. :param providers: list of execution providers The list of providers is ordered by Priority. For example ['CUDAExecutionProvider', 'CPUExecutionProvider'] means execute a node using CUDAExecutionProvider if capable, otherwise execute using CPUExecutionProvider. """ if not set(providers).issubset(C.get_available_providers()): raise ValueError("{} does not contain a subset of available providers {}".format(providers, C.get_available_providers())) self._reset_session() self._load_model(providers)
[docs] def disable_fallback(self): """ Disable session.run() fallback mechanism. """ self._enable_fallback = False
[docs] def enable_fallback(self): """ Enable session.Run() fallback mechanism. If session.Run() fails due to an internal Execution Provider failure, reset the Execution Providers enabled for this session. If GPU is enabled, fall back to CUDAExecutionProvider. otherwise fall back to CPUExecutionProvider. """ self._enable_fallback = True
[docs] def run(self, output_names, input_feed, run_options=None): """ Compute the predictions. :param output_names: name of the outputs :param input_feed: dictionary ``{ input_name: input_value }`` :param run_options: See :class:`onnxruntime.RunOptions`. :: sess.run([output_name], {input_name: x}) """ num_required_inputs = len(self._inputs_meta) num_inputs = len(input_feed) # the graph may have optional inputs used to override initializers. allow for that. if num_inputs < num_required_inputs: raise ValueError("Model requires {} inputs. Input Feed contains {}".format(num_required_inputs, num_inputs)) if not output_names: output_names = [output.name for output in self._outputs_meta] try: return self._sess.run(output_names, input_feed, run_options) except C.EPFail as err: if self._enable_fallback: print("EP Error: {} using {}".format(str(err), self._providers)) print("Falling back to {} and retrying.".format(self._fallback_providers)) self.set_providers(self._fallback_providers) # Fallback only once. self.disable_fallback() return self._sess.run(output_names, input_feed, run_options) else: raise
[docs] def end_profiling(self): """ End profiling and return results in a file. The results are stored in a filename if the option :meth:`onnxruntime.SessionOptions.enable_profiling`. """ return self._sess.end_profiling()