ONNX Model Predict - Predict responses using pretrained Python ONNX model - Simulink (original) (raw)

Predict responses using pretrained Python ONNX model

Since R2024a

Libraries:
Deep Learning Toolbox / Python Neural Networks

Description

The ONNX Model Predict block predicts responses using a pretrained Python® ONNX™ model running in the MATLAB® Python environment. MATLAB supports the reference implementation of Python, often called CPython. If you are on a Mac or Linux® platform, you already have Python installed. If you use a Mac or Linux® platform, you already have Python installed. If you use Windows®, you need to install a distribution, such as those found at https://www.python.org/downloads/. For more information, see Configure Your System to Use Python. Your MATLAB Python environment must have theonnxruntime module installed. The ONNX Model Predict block has been tested using Python version 3.10 and onnxruntime version 1.14.1.

Load a Python model into the block by specifying the path to an ONNX model file that you saved in Python. You can optionally load a Python function to preprocess the input data that Simulink® passes to the Python model, and a Python function to postprocess the predicted responses from the model.

The input port In1 receives input data, optionally rearranges the input array dimensions, and converts the input data to a Python array. The preprocessing function (if specified) processes the converted data in Python and passes it to the ONNX model. The model generates predicted responses for the input data in Python and passes the responses to the Python postprocessing function (if specified). The output portOut1 returns the predicted responses.

You can add and configure input and output ports using the Inputs andOutputs tabs of the Block Parameters dialog box (see Inputs and Outputs). The software attempts to automatically populate the table in each tab when you click the Autofill Fields From Model File button on the Specify model file tab.

Note

You cannot run the ONNX Model Predict block in Rapid Accelerator mode.

Examples

Ports

Input

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Input data, specified as a numeric array. You can rearrange the dimensions of the input data that the block passes to the Python model by specifying a permutation vector on the Inputs tab of the Block Parameters dialog box (seeInputs).

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Output

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Predicted responses, returned as a numeric array. You can rearrange the dimensions of the output data returned by the Python model by specifying a permutation vector on the Outputs tab of the Block Parameters dialog box (see Outputs).

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean

Parameters

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To edit block parameters interactively, use theProperty Inspector. From the Simulink Toolstrip, on the Simulation tab, in thePrepare gallery, select Property Inspector.

Specify model file

Specify the name or path to a Python ONNX model file, or click theBrowse button.

Programmatic Use

Prioritized list of ONNX runtime execution providers, specified as a comma-separated list. The ONNX Model Predict block uses the first execution provider in the list. If the provider is not in the MATLAB Python environment, the block uses the next provider in the list. For more information, see https://onnxruntime.ai/docs/execution-providers/.

To specify CUDAExecutionProvider in the Execution Providers list, you must first install Parallel Computing Toolbox™ and have a GPU device. For information on supported GPU devices, seeGPU Computing Requirements (Parallel Computing Toolbox).

Programmatic Use

Specify the discrete interval between sample time hits or specify another type of sample time, such as continuous (0) or inherited (–1). For more options, see Types of Sample Time (Simulink).

By default, the ONNX Model Predict block inherits sample time based on the context of the block within the model.

Programmatic Use

Inputs

Input port properties, specified as a table. Each row of the table corresponds to an individual input port of the ONNX Model Predict block. The software attempts to automatically populate the input port properties table when you click theAutofill Fields From Model File button on the Specify model file tab.

Double-click a table cell entry to edit its value, and use theUp and Down buttons to reorder the table rows. Add and delete input ports by clicking the New andDelete buttons, respectively. If you specify multiple input ports, their order must correspond to the input order in the Python model (or Python preprocessing function, if specified).

The table has the following columns:

• Input Name — Block input port label, specified as a character vector. The block does not pass the input port label to the Python model.
• Python Datatype — Python or NumPy datatype to which the ONNX Model Predict block converts incoming data before passing it to Python, specified as a character vector. The block supports the Python numeric datatypes "int" and "float", and the NumPy numeric datatypes "float16","float32", "float64","int8", "uint8","int16", "uint16","int32", "uint32","int64", and "uint64". The default value is "float32".
• Permutation to Python — New dimension arrangement for the input data, specified as a numeric vector with unique positive integer elements that represent the dimensions of the input data (see permute). For example, if the input data is a 2D matrix, you can specify [2 1] to switch the row and column dimensions. The ONNX Model Predict block passes the rearranged array to the Python model (or Python preprocessing function, if specified).
• Python NumDims — Number of dimensions for the input data, specified as a nonnegative integer. The default value is"inherit", which corresponds to the dimensionality of the Simulink input signal. The ONNX Model Predict block converts the input data in Simulink to a Python array with the specified dimensions, and then passes the data to the Python predict() function (or Python preprocessing function, if specified). The input data cannot contain more dimensions than the specified number, unless the extra dimensions are singletons. The block does not pass these extra singleton dimensions to Python. If the input data has fewer dimensions than the specified number, the block adds trailing singleton dimensions, as needed.

Programmatic Use

Outputs

Output port properties, specified as a table. Each row of the table corresponds to an individual output port of the ONNX Model Predict block. The software attempts to automatically populate the output port properties table when you click theAutofill Fields From Model File button on the Specify model file tab.

Double-click a table cell entry to edit its value, and use theUp and Down buttons to reorder the table rows. Add or delete output ports by clicking the New andDelete buttons, respectively. If you specify multiple output ports, their order must correspond to the output order in the Python model (or Python postprocessing function, if specified).

The table has the following columns:

• Output Name — Block output port label, specified as a character vector.
• Permutation from Python — New dimension ordering for the array that the ONNX Model Predict block passes to the output port, specified as a numeric vector with unique positive integer elements (see permute).
• Max MATLAB Dim Sizes — Maximum size of the block output along each dimension, specified as an array of positive integers. Specify this parameter only if your Python model is capable of returning variable sized output during a single simulation run.

Programmatic Use

Pre/Post-processing

Specify the name or path of a file defining an optional Python preprocessing function for the input data, or click the Browse button. When the ONNX Model Predict receives data, it converts it to a NumPy array. The preprocessing function processes the converted data in Python, and then passes the data to the Python model.

The preprocessing file must define a Python function with the signature

outputList = preprocess(model,inputList)

whereoutputList and inputList are lists of NumPyndarray objects, and model is the Python model object. The number of elements in inputList must match the number of input ports in the ONNX Model Predict block. The number of elements inoutputList must match the number of inputs required by the Python model. The preprocessing file can be the same as the postprocessing file specified byPath to Python file defining postprocess() if the file contains both defining functions.

Programmatic Use

Specify the name or path of a file defining an optional Python postprocessing function for the output data, or click the Browse button. TheONNX Model Predict block processes the output data from the Python model using the Python function, and then outputs the data from the block.

The postprocessing file must define a Python function with the signature

outputList = postprocess(model,inputList)

whereoutputList and inputList are lists of NumPyndarray objects, and model is the model object. The number of elements in inputList must match the number of outputs in the Python model. The number of elements inoutputList must match the number of output ports in theONNX Model Predict block. The postprocessing file can be the same as the preprocessing specified by Path to Python file defining preprocess() if the file contains both defining functions.

Programmatic Use

Block Characteristics

Version History

Introduced in R2024a

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Specify the execution providers for an ONNX model using the ONNX Runtime Execution Providers field on the Specify model file tab. Include CUDAExecutionProvider in the list to execute the model on a GPU.