ONNX Runtime inference in PyTorch - Model Pipeline Trace

Model Pipeline - ONNX Runtime inference

This pipeline shows how a PyTorch model is converted to ONNX format and then used for fast inference with ONNX Runtime. It helps run the model efficiently outside PyTorch.

Data Flow - 4 Stages

1Original PyTorch Model

1 row x 3 channels x 224 height x 224 width→Define and train a CNN model in PyTorch→1 row x 1000 classes

Input: image tensor with shape (1, 3, 224, 224), Output: logits for 1000 classes

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2Export to ONNX

1 row x 3 x 224 x 224→Convert PyTorch model to ONNX format file→ONNX model file with same input/output shapes

ONNX file saved as model.onnx representing the CNN

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3Load ONNX Model in ONNX Runtime

1 row x 3 x 224 x 224→Load ONNX model for inference using ONNX Runtime session→Session ready to accept input tensor

ONNX Runtime session created from model.onnx

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4Run Inference

1 row x 3 x 224 x 224→Feed input tensor to ONNX Runtime session to get predictions→1 row x 1000 classes

Output: array of class scores from ONNX Runtime

Training Trace - Epoch by Epoch

Loss
1.8 |*       
1.2 |  *     
0.8 |    *   
    +--------
     1  5  10
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.8	0.35	Initial training with high loss and low accuracy
5	1.2	0.60	Loss decreasing and accuracy improving steadily
10	0.8	0.75	Model converging with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input tensor preparation

Layer 2: ONNX Runtime session run

Layer 3: Softmax conversion

Layer 4: Prediction selection

Model Quiz - 3 Questions

What is the main benefit of using ONNX Runtime for inference?

A Automatically increases dataset size

B Improves model training accuracy

C Faster and platform-independent model execution

D Converts images to grayscale

Key Insight

Converting a PyTorch model to ONNX format allows running the model efficiently on different platforms using ONNX Runtime. This separation of training and inference environments helps deploy models faster and with lower latency.