ONNX Runtime in Computer Vision

Introduction

ONNX Runtime helps you run machine learning models fast and easily on many devices. It makes using models from different tools simple.

You want to run a trained model on your computer without retraining.

You have a model from another tool and want to use it in your app.

You want faster predictions on images or text using a ready model.

You want to run the same model on different devices like PC or phone.

You want to test how well a model works on new data quickly.

Syntax

Computer Vision

import onnxruntime as ort

# Load the model
session = ort.InferenceSession('model.onnx')

# Prepare input data as a dictionary
inputs = {'input_name': input_array}

# Run the model to get outputs
outputs = session.run(None, inputs)

Replace 'model.onnx' with your ONNX model file path.

Input names must match the model's expected input names.

Examples

Load a handwritten digit model and run it on random image data.

Computer Vision

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession('mnist.onnx')
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1, 1, 28, 28).astype('float32')
outputs = session.run(None, {input_name: input_data})

Run the model and print the first output array.

Computer Vision

import onnxruntime as ort

session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0].name
inputs = {input_name: your_numpy_array}
outputs = session.run(None, inputs)
print(outputs[0])

Sample Model

This code loads an ONNX model that adds 1 to each input number. It runs the model on three numbers and prints the results.

Computer Vision

import onnxruntime as ort
import numpy as np

# Load a simple ONNX model (for example, a model that adds 1 to input)
# Here we create a dummy input and run the model
session = ort.InferenceSession('add_one.onnx')

# Get the input name from the model
input_name = session.get_inputs()[0].name

# Create input data: a batch of 3 numbers
input_data = np.array([[1.0], [2.0], [3.0]], dtype=np.float32)

# Run the model
outputs = session.run(None, {input_name: input_data})

# Print the output
print('Input:', input_data)
print('Output:', outputs[0])

Important Notes

ONNX Runtime supports many hardware accelerations for faster results.

Always check the input and output names with session.get_inputs() and session.get_outputs().

Input data must match the model's expected shape and data type.

Summary

ONNX Runtime lets you run machine learning models easily on many devices.

You load a model, prepare inputs, and get outputs with simple Python code.

It works well for quick testing and deploying models without extra training.

Practice

1. What is the main purpose of ONNX Runtime in machine learning?

easy

A. To collect and label training data

B. To train new machine learning models from scratch

C. To visualize data and create charts

D. To run pre-trained machine learning models efficiently on different devices

Solution

1. Step 1: Understand ONNX Runtime's role

   ONNX Runtime is designed to run models that are already trained, not to train new ones.

2. Step 2: Identify the correct purpose

   It helps run these models efficiently on many devices, making deployment easier.

3. Final Answer:

   To run pre-trained machine learning models efficiently on different devices -> Option D

4. Quick Check:

   ONNX Runtime runs models = A [OK]

Hint: ONNX Runtime runs models, not trains them [OK]

Common Mistakes:

• Confusing ONNX Runtime with training frameworks
• Thinking it is for data visualization
• Assuming it collects or labels data

2. Which Python code snippet correctly loads an ONNX model using ONNX Runtime?

easy

A. import onnxruntime as ort
session = ort.Model('model.onnx')

B. import onnxruntime as ort
session = ort.load_model('model.onnx')

C. import onnxruntime as ort
session = ort.InferenceSession('model.onnx')

D. import onnxruntime as ort
session = ort.run('model.onnx')

Solution

1. Step 1: Recall ONNX Runtime loading method

   The correct method to load a model is using InferenceSession with the model file path.

2. Step 2: Check each option

   Only import onnxruntime as ort session = ort.InferenceSession('model.onnx') uses ort.InferenceSession correctly; others use invalid methods.

3. Final Answer:

   import onnxruntime as ort\nsession = ort.InferenceSession('model.onnx') -> Option C

4. Quick Check:

   Use InferenceSession to load model = A [OK]

Hint: Use ort.InferenceSession('model.onnx') to load model [OK]

Common Mistakes:

• Using non-existent methods like load_model or run
• Not importing onnxruntime correctly
• Confusing model loading with running

3. Given the code below, what will be the output type of outputs?

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1, 3, 224, 224).astype(np.float32)
outputs = session.run(None, {input_name: input_data})
print(type(outputs))

medium

A.

B.

C.

D.

Solution

1. Step 1: Understand session.run output

   Calling session.run returns a list of outputs from the model.

2. Step 2: Check the print statement

   Printing type(outputs) will show <class 'list'> because outputs is a list.

3. Final Answer:

   <class 'list'> -> Option A

4. Quick Check:

   session.run returns list = C [OK]

Hint: session.run returns a list of outputs [OK]

Common Mistakes:

• Assuming outputs is a numpy array directly
• Thinking outputs is a dictionary
• Confusing tuple with list

4. Identify the error in the following ONNX Runtime code snippet:

import onnxruntime as ort
session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0]
input_data = [1.0, 2.0, 3.0]
outputs = session.run(None, {input_name: input_data})

medium

A. input_name should be the name string, not the input object

B. input_data must be a dictionary, not a list

C. session.run requires the model path as first argument

D. onnxruntime does not support list inputs

Solution

1. Step 1: Check input_name assignment

   session.get_inputs()[0] returns an input object, but session.run expects the input name string as key.

2. Step 2: Correct usage

   Use session.get_inputs()[0].name to get the input name string for the dictionary key.

3. Final Answer:

   input_name should be the name string, not the input object -> Option A

4. Quick Check:

   Use input_name = session.get_inputs()[0].name [OK]

Hint: Use input_name = session.get_inputs()[0].name [OK]

Common Mistakes:

• Using input object instead of input name string
• Passing wrong input data types
• Misunderstanding session.run arguments

5. You want to run an ONNX model on a GPU using ONNX Runtime. Which code snippet correctly enables GPU execution?

hard

A. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', execution_mode='GPU')

B. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])

C. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', use_gpu=True)

D. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', device='GPU')

Solution

1. Step 1: Recall how to enable GPU in ONNX Runtime

   ONNX Runtime uses the 'providers' argument with 'CUDAExecutionProvider' to run on GPU.

2. Step 2: Check each option

   Only import onnxruntime as ort session = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider']) correctly uses providers=['CUDAExecutionProvider']; others use invalid parameters.

3. Final Answer:

   import onnxruntime as ort\nsession = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider']) -> Option B

4. Quick Check:

   Use providers=['CUDAExecutionProvider'] for GPU [OK]

Hint: Set providers=['CUDAExecutionProvider'] to use GPU [OK]

Common Mistakes:

• Using non-existent parameters like device or use_gpu
• Confusing execution_mode with providers
• Not specifying providers disables GPU