ONNX Runtime in Computer Vision - Model Pipeline Trace

Model Pipeline - ONNX Runtime

ONNX Runtime helps run machine learning models fast and efficiently. It takes a trained model and uses it to make predictions on new images quickly.

Data Flow - 4 Stages

1Input Image

1 image x 224 x 224 x 3 channels→Load and resize image to 224x224 pixels with 3 color channels (RGB)→1 image x 224 x 224 x 3 channels

A photo of a cat resized to 224x224 pixels

↓

2Preprocessing

1 image x 224 x 224 x 3 channels→Normalize pixel values to range 0-1 and reorder dimensions to match model input→1 image x 3 channels x 224 x 224

Pixel values converted from 0-255 to 0-1 and channels moved to first dimension

↓

3ONNX Runtime Model Inference

1 image x 3 channels x 224 x 224→Run the ONNX model to predict image class probabilities→1 image x 1000 classes

Model outputs probabilities for 1000 possible object classes

↓

4Postprocessing

1 image x 1000 classes→Select class with highest probability as prediction→1 predicted class label

Predicted label: 'tabby cat'

Training Trace - Epoch by Epoch

Epoch 1: *************** (loss=1.8)
Epoch 2: ************ (loss=1.2)
Epoch 3: ********* (loss=0.9)
Epoch 4: ******* (loss=0.7)
Epoch 5: ****** (loss=0.6)

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.8	0.35	Model starts learning with high loss and low accuracy
2	1.2	0.55	Loss decreases and accuracy improves as model learns features
3	0.9	0.70	Model continues to improve, learning better representations
4	0.7	0.78	Loss lowers steadily, accuracy nearing good performance
5	0.6	0.82	Model converges with stable loss and high accuracy

Prediction Trace - 4 Layers

Layer 1: Input Image

Layer 2: Preprocessing

Layer 3: ONNX Runtime Inference

Layer 4: Postprocessing

Model Quiz - 3 Questions

What shape does the image have after preprocessing before model input?

A 224 x 224 x 3 channels

B 1 image x 3 channels x 224 x 224

C 1 image x 224 x 224 x 3 channels

D 3 channels x 224 x 224

Key Insight

ONNX Runtime efficiently runs trained models by taking preprocessed images and quickly producing class predictions. The training process shows steady improvement in accuracy as loss decreases, ensuring reliable results during inference.

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