mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
11f0081c1e
* Remove tensorflow, tf2onnx from the list of dependencies for the documentation * improve documentation * update API
247 lines
7.6 KiB
ReStructuredText
247 lines
7.6 KiB
ReStructuredText
|
||
===========
|
||
API Summary
|
||
===========
|
||
|
||
Summary of public functions and classes exposed
|
||
in *ONNX Runtime*.
|
||
|
||
.. contents::
|
||
:local:
|
||
|
||
OrtValue
|
||
=========
|
||
|
||
*ONNX Runtime* works with native Python data structures which are mapped into ONNX data formats :
|
||
Numpy arrays (tensors), dictionaries (maps), and a list of Numpy arrays (sequences).
|
||
The data backing these are on CPU.
|
||
|
||
*ONNX Runtime* supports a custom data structure that supports all ONNX data formats that allows users
|
||
to place the data backing these on a device, for example, on a CUDA supported device. This allows for
|
||
interesting *IOBinding* scenarios (discussed below). In addition, *ONNX Runtime* supports directly
|
||
working with *OrtValue* (s) while inferencing a model if provided as part of the input feed.
|
||
|
||
Below is an example showing creation of an *OrtValue* from a Numpy array while placing its backing memory
|
||
on a CUDA device:
|
||
|
||
.. code-block:: python
|
||
|
||
# X is numpy array on cpu, create an OrtValue and place it on cuda device id = 0
|
||
ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
|
||
ortvalue.device_name() # 'cuda'
|
||
ortvalue.shape() # shape of the numpy array X
|
||
ortvalue.data_type() # 'tensor(float)'
|
||
ortvalue.is_tensor() # 'True'
|
||
np.array_equal(ortvalue.numpy(), X) # 'True'
|
||
|
||
# ortvalue can be provided as part of the input feed to a model
|
||
ses = onnxruntime.InferenceSession('model.onnx')
|
||
res = sess.run(["Y"], {"X": ortvalue})
|
||
|
||
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 device 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()
|
||
# OnnxRuntime will copy the data over to the CUDA device if 'input' is consumed by nodes on the CUDA device
|
||
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 directly use the input. The output data is on CPU.
|
||
|
||
.. code-block:: python
|
||
|
||
# X is numpy array on cpu
|
||
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
|
||
session = onnxruntime.InferenceSession('model.onnx')
|
||
io_binding = session.io_binding()
|
||
io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.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 and output data are both on a device, users directly use the input and also place output on the device.
|
||
|
||
.. code-block:: python
|
||
|
||
#X is numpy array on cpu
|
||
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
|
||
Y_ortvalue = onnxruntime.OrtValue.ortvalue_from_shape_and_type([3, 2], np.float32, 'cuda', 0) # Change the shape to the actual shape of the output being bound
|
||
session = onnxruntime.InferenceSession('model.onnx')
|
||
io_binding = session.io_binding()
|
||
io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
|
||
io_binding.bind_output(name='output', device_type=Y_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=Y_ortvalue.shape(), buffer_ptr=Y_ortvalue.data_ptr())
|
||
session.run_with_iobinding(io_binding)
|
||
|
||
Scenario 4:
|
||
|
||
Users can request *ONNX Runtime* to allocate an output on a device. This is particularly useful for dynamic shaped outputs.
|
||
Users can use the *get_outputs()* API to get access to the *OrtValue* (s) corresponding to the allocated output(s).
|
||
Users can thus consume the *ONNX Runtime* allocated memory for the output as an *OrtValue*.
|
||
|
||
.. code-block:: python
|
||
|
||
#X is numpy array on cpu
|
||
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
|
||
session = onnxruntime.InferenceSession('model.onnx')
|
||
io_binding = session.io_binding()
|
||
io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
|
||
#Request ONNX Runtime to bind and allocate memory on CUDA for 'output'
|
||
io_binding.bind_output('output', 'cuda')
|
||
session.run_with_iobinding(io_binding)
|
||
# The following call returns an OrtValue which has data allocated by ONNX Runtime on CUDA
|
||
ort_output = io_binding.get_outputs()[0]
|
||
|
||
|
||
Scenario 5:
|
||
|
||
Users can bind *OrtValue* (s) directly.
|
||
|
||
.. code-block:: python
|
||
|
||
#X is numpy array on cpu
|
||
#X is numpy array on cpu
|
||
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
|
||
Y_ortvalue = onnxruntime.OrtValue.ortvalue_from_shape_and_type([3, 2], np.float32, 'cuda', 0) # Change the shape to the actual shape of the output being bound
|
||
session = onnxruntime.InferenceSession('model.onnx')
|
||
io_binding = session.io_binding()
|
||
io_binding.bind_ortvalue_input('input', X_ortvalue)
|
||
io_binding.bind_ortvalue_output('output', Y_ortvalue)
|
||
session.run_with_iobinding(io_binding)
|
||
|
||
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.
|
||
|
||
Main class
|
||
----------
|
||
|
||
.. autoclass:: onnxruntime.InferenceSession
|
||
:members:
|
||
:inherited-members:
|
||
|
||
Options
|
||
-------
|
||
|
||
RunOptions
|
||
^^^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.RunOptions
|
||
:members:
|
||
|
||
SessionOptions
|
||
^^^^^^^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.SessionOptions
|
||
:members:
|
||
|
||
Data
|
||
----
|
||
|
||
OrtValue
|
||
^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.OrtValue
|
||
:members:
|
||
|
||
SparseTensor
|
||
^^^^^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.SparseTensor
|
||
:members:
|
||
|
||
Devices
|
||
-------
|
||
|
||
IOBinding
|
||
^^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.IOBinding
|
||
:members:
|
||
|
||
OrtDevice
|
||
^^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.OrtDevice
|
||
:members:
|
||
|
||
Internal classes
|
||
----------------
|
||
|
||
These classes cannot be instantiated by users but they are returned
|
||
by methods or functions of this libary.
|
||
|
||
ModelMetadata
|
||
^^^^^^^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.ModelMetadata
|
||
:members:
|
||
|
||
NodeArg
|
||
^^^^^^^
|
||
|
||
.. autoclass:: onnxruntime.NodeArg
|
||
: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
|