ONNX Runtime in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an ONNX model using ONNX Runtime.

Computer Vision

import onnxruntime as ort

session = ort.InferenceSession([1])

Drag options to blanks, or click blank then click option'

A"model.onnx"

Bmodel.onnx

Cload_model("model.onnx")

Donnx.load("model.onnx")

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the model filename without quotes causes a NameError.

Trying to use onnx.load instead of passing the file path.

The InferenceSession expects the model file path as a string.

2fill in blank

medium

Complete the code to prepare input data for ONNX Runtime inference.

Computer Vision

import numpy as np

input_name = session.get_inputs()[0].name
input_data = np.random.rand(1, 3, 224, 224).astype([1])

Drag options to blanks, or click blank then click option'

A"int32"

B"float64"

C"uint8"

D"float32"

Attempts:

3 left

💡 Hint

Common Mistakes

Using float64 causes type mismatch errors.

Using integer types when the model expects floats.

ONNX models usually expect input data as 32-bit floats (float32).

3fill in blank

hard

Fix the error in the code to run inference and get the output.

Computer Vision

outputs = session.run(None, [1])
print(outputs[0])

Drag options to blanks, or click blank then click option'

A(input_name, input_data)

B{input_name: input_data}

Cinput_data

D[input_name, input_data]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing inputs as a list or tuple causes runtime errors.

Passing only the input data without the input name.

The run method expects a dictionary mapping input names to input data.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps input names to numpy arrays.

Computer Vision

inputs = { [1]: np.array(value) for [2], value in input_data.items() }

Drag options to blanks, or click blank then click option'

Aname

Bkey

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for the key and the iteration variable.

Not using a colon between key and value.

Dictionary comprehension syntax: {key: value for key, value in dict.items()}.

5fill in blank

hard

Fill all three blanks to extract output names, run inference, and print the first output.

Computer Vision

output_names = [output.name for output in session.get_outputs()]
results = session.run([1], [2])
print(results[3])

Drag options to blanks, or click blank then click option'

Aoutput_names

B{input_name: input_data}

C[0]

Dinput_data

Attempts:

3 left

💡 Hint

Common Mistakes

Passing inputs as a list instead of a dictionary.

Not specifying output names in the run method.

Trying to print results without indexing.

Use output_names to specify outputs, input dictionary for inputs, and index [0] to print first output.

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