2018-11-20 00:48:22 +00:00
|
|
|
|
|
|
|
|
|
|
===========
|
|
|
|
|
|
API Summary
|
|
|
|
|
|
===========
|
|
|
|
|
|
|
|
|
|
|
|
Summary of public functions and classes exposed
|
|
|
|
|
|
in *ONNX Runtime*.
|
|
|
|
|
|
|
|
|
|
|
|
.. contents::
|
|
|
|
|
|
:local:
|
|
|
|
|
|
|
2020-07-08 10:48:22 +00:00
|
|
|
|
IOBinding
|
|
|
|
|
|
=========
|
|
|
|
|
|
|
|
|
|
|
|
By default, *ONNX Runtime* always places input(s) and output(s) on CPU, which
|
|
|
|
|
|
is not optimal if the input or output is consumed and produced on a device
|
|
|
|
|
|
other than CPU because it introduces data copy between CPU and the device.
|
|
|
|
|
|
*ONNX Runtime* provides a feature, *IO Binding*, which addresses this issue by
|
|
|
|
|
|
enabling users to specify which device to place input(s) and output(s) on.
|
|
|
|
|
|
Here are scenarios to use this feature.
|
|
|
|
|
|
|
|
|
|
|
|
(In the following code snippets, *model.onnx* is the model to execute,
|
|
|
|
|
|
*X* is the input data to feed, and *Y* is the output data.)
|
|
|
|
|
|
|
|
|
|
|
|
Scenario 1:
|
|
|
|
|
|
|
|
|
|
|
|
A graph is executed on a deivce other than CPU, for instance CUDA. Users can
|
|
|
|
|
|
use IOBinding to put input on CUDA as the follows.
|
|
|
|
|
|
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
|
|
|
|
|
|
|
|
#X is numpy array on cpu
|
|
|
|
|
|
session = onnxruntime.InferenceSession('model.onnx')
|
|
|
|
|
|
io_binding = session.io_binding()
|
|
|
|
|
|
io_binding.bind_cpu_input('input', X)
|
|
|
|
|
|
io_binding.bind_output('output')
|
|
|
|
|
|
session.run_with_iobinding(io_binding)
|
|
|
|
|
|
Y = io_binding.copy_outputs_to_cpu()[0]
|
|
|
|
|
|
|
|
|
|
|
|
Scenario 2:
|
|
|
|
|
|
|
|
|
|
|
|
The input data is on a device, users direclty use the input. The output data is on CPU.
|
|
|
|
|
|
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
|
|
|
|
|
|
|
|
session = onnxruntime.InferenceSession('model.onnx')
|
|
|
|
|
|
io_binding = session.io_binding()
|
|
|
|
|
|
io_binding.bind_input(name='input', device_type=X.device.type, device_id=0, element_type=np.float32, shape=list(X.size()), buffer_ptr=X.data_ptr())
|
|
|
|
|
|
io_binding.bind_output('output')
|
|
|
|
|
|
session.run_with_iobinding(io_binding)
|
|
|
|
|
|
Y = io_binding.copy_outputs_to_cpu()[0]
|
|
|
|
|
|
|
|
|
|
|
|
Scenario 3:
|
|
|
|
|
|
|
|
|
|
|
|
The input data on a dveice, users directly use the input and also place output on the device:
|
|
|
|
|
|
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
|
|
|
|
|
|
|
|
session = onnxruntime.InferenceSession('model.onnx')
|
|
|
|
|
|
io_binding = session.io_binding()
|
|
|
|
|
|
io_binding.bind_input(name='input', device_type=X.device.type, device_id=0, element_type=np.float32, shape=list(X.size()), buffer_ptr=X.data_ptr())
|
|
|
|
|
|
io_binding.bind_output(name='output', device_type=Y.device.type, device_id=0, element_type=np.float32, shape=list(Y.size()), buffer_ptr=Y.data_ptr())
|
|
|
|
|
|
session.run_with_iobinding(io_binding)
|
|
|
|
|
|
|
2018-11-20 00:48:22 +00:00
|
|
|
|
Device
|
|
|
|
|
|
======
|
|
|
|
|
|
|
|
|
|
|
|
The package is compiled for a specific device, GPU or CPU.
|
|
|
|
|
|
The CPU implementation includes optimizations
|
|
|
|
|
|
such as MKL (Math Kernel Libary). The following function
|
|
|
|
|
|
indicates the chosen option:
|
|
|
|
|
|
|
|
|
|
|
|
.. autofunction:: onnxruntime.get_device
|
|
|
|
|
|
|
|
|
|
|
|
Examples and datasets
|
|
|
|
|
|
=====================
|
|
|
|
|
|
|
|
|
|
|
|
The package contains a few models stored in ONNX format
|
|
|
|
|
|
used in the documentation. These don't need to be downloaded
|
|
|
|
|
|
as they are installed with the package.
|
|
|
|
|
|
|
|
|
|
|
|
.. autofunction:: onnxruntime.datasets.get_example
|
|
|
|
|
|
|
|
|
|
|
|
Load and run a model
|
|
|
|
|
|
====================
|
|
|
|
|
|
|
|
|
|
|
|
*ONNX Runtime* reads a model saved in ONNX format.
|
|
|
|
|
|
The main class *InferenceSession* wraps these functionalities
|
|
|
|
|
|
in a single place.
|
|
|
|
|
|
|
|
|
|
|
|
.. autoclass:: onnxruntime.ModelMetadata
|
|
|
|
|
|
:members:
|
|
|
|
|
|
|
|
|
|
|
|
.. autoclass:: onnxruntime.InferenceSession
|
|
|
|
|
|
:members:
|
|
|
|
|
|
|
|
|
|
|
|
.. autoclass:: onnxruntime.NodeArg
|
|
|
|
|
|
:members:
|
|
|
|
|
|
|
|
|
|
|
|
.. autoclass:: onnxruntime.RunOptions
|
|
|
|
|
|
:members:
|
|
|
|
|
|
|
|
|
|
|
|
.. autoclass:: onnxruntime.SessionOptions
|
|
|
|
|
|
:members:
|
|
|
|
|
|
|
|
|
|
|
|
Backend
|
|
|
|
|
|
=======
|
|
|
|
|
|
|
|
|
|
|
|
In addition to the regular API which is optimized for performance and usability,
|
|
|
|
|
|
*ONNX Runtime* also implements the
|
|
|
|
|
|
`ONNX backend API <https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md>`_
|
|
|
|
|
|
for verification of *ONNX* specification conformance.
|
|
|
|
|
|
The following functions are supported:
|
|
|
|
|
|
|
|
|
|
|
|
.. autofunction:: onnxruntime.backend.is_compatible
|
|
|
|
|
|
|
|
|
|
|
|
.. autofunction:: onnxruntime.backend.prepare
|
|
|
|
|
|
|
|
|
|
|
|
.. autofunction:: onnxruntime.backend.run
|
|
|
|
|
|
|
|
|
|
|
|
.. autofunction:: onnxruntime.backend.supports_device
|
