ONNX export in PyTorch - Model Pipeline Trace

Model Pipeline - ONNX export

This pipeline shows how a PyTorch model is trained and then exported to the ONNX format. ONNX allows the model to be used in different environments outside PyTorch.

Data Flow - 5 Stages

1Raw Data

1000 rows x 10 columns→Collect raw input features and labels→1000 rows x 10 columns

[[0.5, 1.2, ..., 0.3], label=1]

↓

2Preprocessing

1000 rows x 10 columns→Normalize features to zero mean and unit variance→1000 rows x 10 columns

[[0.0, 0.8, ..., -0.5], label=1]

↓

3Train/Test Split

1000 rows x 10 columns→Split data into training (80%) and testing (20%) sets→Train: 800 rows x 10 columns, Test: 200 rows x 10 columns

Train sample: [[0.1, -0.3, ..., 0.2], label=0]

↓

4Model Training

800 rows x 10 columns→Train a simple feedforward neural network→Trained model with input size 10 and output size 2

Model weights updated after each batch

↓

5ONNX Export

Single input tensor of shape [1, 10]→Export trained PyTorch model to ONNX format→ONNX model file with input shape [1, 10] and output shape [1, 2]

ONNX file saved as 'model.onnx'

Training Trace - Epoch by Epoch

Loss
0.7 |*       
0.6 | *      
0.5 |  *     
0.4 |   *    
0.3 |    *   
0.2 |     *  
0.1 |       
    +--------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Loss starts high, accuracy is low but learning begins
2	0.48	0.75	Loss decreases, accuracy improves
3	0.35	0.82	Model continues to learn, better predictions
4	0.28	0.87	Loss decreases steadily, accuracy rises
5	0.22	0.90	Training converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU)

Layer 3: Output Layer (Softmax)

Model Quiz - 3 Questions

What happens to the data shape after preprocessing?

A Shape stays the same but values are normalized

B Number of columns doubles

C Rows are reduced by half

D Data is converted to categorical

Key Insight

Exporting a trained PyTorch model to ONNX format allows the model to be used in many other tools and platforms, making it flexible and portable beyond PyTorch environments.