Using ONNX for Cross-Framework AI Development with PyTorch and TensorFlow

A Comprehensive Guide to utilizing ONNX to build, train, test, save, and load AI models between PyTorch and TensorFlow AI Frameworks

Introduction to ONNX

ONNX (Open Neural Network Exchange) is an open-source format for machine learning (ML) and deep learning models that enables interoperability across different frameworks. ONNX allows models trained in one framework (such as PyTorch, TensorFlow, or Scikit-Learn) to be exported and used in another framework, making it easier to deploy models in diverse environments.

Why Use ONNX?

• Interoperability: Transfer models across frameworks (e.g., train in PyTorch and run in TensorFlow).

• Optimization: Use ONNX Runtime for optimized inference.

• Cross-platform Compatibility: Deploy models on different platforms (edge devices, web, cloud).

• Efficiency: Faster inference compared to framework-specific formats.

This article provides a step-by-step guide on how to install ONNX, train a model, test it, and save it in the ONNX format.

---

Step 1: Installing ONNX

To start using ONNX, we need to install the necessary packages.

1.1 Install ONNX and ONNX Runtime

pip install onnx onnxruntime

1.2 Install ONNX Support for PyTorch and TensorFlow

If you plan to export models from PyTorch or TensorFlow:

• For PyTorch:

pip install torch torchvision onnx onnxruntime

• For TensorFlow:

pip install tf2onnx

Verify installation:

import onnx
print("ONNX version:", onnx.__version__)

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Step 2: Train a Model and Convert It to ONNX

To demonstrate ONNX, we will train a simple neural network using PyTorch to classify XOR logic.

2.1 Train an XOR Neural Network in PyTorch

Create a file train_xor.py:

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Define XOR dataset
X = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=torch.float32)
y = torch.tensor([[0], [1], [1], [0]], dtype=torch.float32)

# Define the Neural Network
class XORNet(nn.Module):
    def __init__(self):
        super(XORNet, self).__init__()
        self.hidden = nn.Linear(2, 4)
        self.output = nn.Linear(4, 1)
    
    def forward(self, x):
        x = torch.sigmoid(self.hidden(x))
        x = torch.sigmoid(self.output(x))
        return x

# Instantiate model
model = XORNet()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.1)

# Train model
epochs = 5000
for epoch in range(epochs):
    optimizer.zero_grad()
    output = model(X)
    loss = criterion(output, y)
    loss.backward()
    optimizer.step()

    if epoch % 500 == 0:
        print(f'Epoch {epoch}, Loss: {loss.item()}')

print("Training complete!")

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Step 3: Convert and Save the Model in ONNX Format

Once training is complete, we export the PyTorch model to ONNX.

3.1 Define Model Export Function

Modify train_xor.py to include the following:

# Convert model to ONNX format
def save_onnx_model():
    model.eval()  # Set to evaluation mode
    dummy_input = torch.tensor([[0.0, 0.0]])  # Dummy input for tracing
    torch.onnx.export(model, dummy_input, "xor_model.onnx", input_names=["input"], output_names=["output"])
    print("Model saved as XOR_model.onnx")

save_onnx_model()

Run:

python train_xor.py

This generates xor_model.onnx, which can now be used for inference.

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Step 4: Load and Test the ONNX Model

To ensure our ONNX model works, we will load it using ONNX Runtime and run inference.

4.1 Load ONNX Model

Create a new script test_onnx.py:

import onnx
import onnxruntime as ort
import numpy as np

# Load ONNX model
onnx_model = onnx.load("xor_model.onnx")
onnx.checker.check_model(onnx_model)  # Validate model
print("ONNX model loaded successfully!")

# Run inference
session = ort.InferenceSession("xor_model.onnx")

# Define XOR inputs
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
onnx_inputs = {session.get_inputs()[0].name: inputs}

# Get predictions
outputs = session.run(None, onnx_inputs)

# Print results
print("ONNX Model Predictions:")
for i, inp in enumerate(inputs):
    print(f"Input: {inp} -> Prediction: {outputs[0][i][0]:.4f}")

Run:

python test_onnx.py

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Step 5: Expected Output

After running test_onnx.py, you should see an output similar to this:

ONNX model loaded successfully!
ONNX Model Predictions:
Input: [0. 0.] -> Prediction: 0.02
Input: [0. 1.] -> Prediction: 0.97
Input: [1. 0.] -> Prediction: 0.98
Input: [1. 1.] -> Prediction: 0.03

This confirms that the ONNX model correctly predicts the XOR logic.

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Step 6: Deploying the ONNX Model

Once converted, the ONNX model can be used in multiple environments:

• Run it in a Web Browser using ONNX.js

• Deploy it on Edge Devices (Raspberry Pi, Jetson Nano)

• Integrate with TensorFlow, ML.NET, or other frameworks

To use it in a web browser, include ONNX.js:

<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
<script>
  async function runModel() {
    let session = await ort.InferenceSession.create("xor_model.onnx");
    let inputs = new ort.Tensor("float32", [0, 1], [1, 2]);
    let results = await session.run({ input: inputs });
    console.log("Prediction:", results.output.data);
  }
  runModel();
</script>

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Conclusion

ONNX is a powerful format for model portability across different ML frameworks. In this guide, we:

• Installed ONNX and its dependencies.

• Trained a simple XOR neural network using PyTorch.

• Converted the trained model into ONNX format.

• Tested the ONNX model using ONNX Runtime.

• Explored how to deploy the model in different environments.

By leveraging ONNX, you can train models in one framework and deploy them anywhere with optimized inference performance.

🚀 Now you're ready to integrate ONNX into your ML workflows! 🚀

Training an XOR Model in TensorFlow and Saving it in ONNX Format

Next, this guide will walk you through how to:

1. Train an XOR neural network using TensorFlow

2. Convert and save the trained model in ONNX format

---

Step 1: Install Required Packages

Before we start, ensure you have the necessary libraries installed. Run the following command in your terminal or command prompt:

pip install tensorflow tf2onnx onnx

• tensorflow: For training the neural network

• tf2onnx: Converts TensorFlow models to ONNX

• onnx: Provides tools to validate the ONNX model

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Step 2: Train an XOR Neural Network in TensorFlow

Create a Python script named train_xor_tf.py and add the following code:

import tensorflow as tf
import numpy as np

# Step 1: Define XOR dataset
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
y = np.array([[0], [1], [1], [0]], dtype=np.float32)

# Step 2: Build a simple neural network model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(4, activation='sigmoid', input_shape=(2,)),  # Hidden layer with 4 neurons
    tf.keras.layers.Dense(1, activation='sigmoid')  # Output layer with 1 neuron
])

# Step 3: Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Step 4: Train the model
model.fit(X, y, epochs=5000, verbose=0)  # Silent training

# Print final predictions
print("\nFinal predictions after training:")
print(model.predict(X).round(4))

# Step 5: Save the trained model in TensorFlow format
model.save("xor_tf_model")
print("\nModel saved as 'xor_tf_model'")

Breakdown of the Code:

1. Define XOR dataset → Input (X) and expected output (y).

2. Create a neural network with:

   • A hidden layer with 4 neurons (sigmoid activation)

   • A single output neuron (sigmoid activation).

3. Compile the model using:

   • Adam optimizer

   • Binary cross-entropy loss (since XOR is a binary classification problem).

4. Train the model for 5000 epochs.

5. Save the trained model using TensorFlow’s model.save().

Run the script:

python train_xor_tf.py

You should see final predictions close to:

Final predictions after training:
[[0.02]
 [0.98]
 [0.98]
 [0.02]]

---

Step 3: Convert TensorFlow Model to ONNX

Now, convert the saved model into ONNX format.

Create a new script named convert_to_onnx.py and add:

import tf2onnx
import tensorflow as tf

# Load the trained TensorFlow model
model = tf.keras.models.load_model("xor_tf_model")

# Convert the model to ONNX
onnx_model_path = "xor_tf_model.onnx"
spec = (tf.TensorSpec((None, 2), tf.float32, name="input"),)  # Define input shape
model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13)

# Save the ONNX model
with open(onnx_model_path, "wb") as f:
    f.write(model_proto.SerializeToString())

print(f"\nModel successfully converted to ONNX and saved as '{onnx_model_path}'")

Breakdown of the Code:

1. Load the trained TensorFlow model using tf.keras.models.load_model().

2. Define the input signature (input size is 2).

3. Convert the model to ONNX using tf2onnx.convert.from_keras().

4. Save the ONNX model as xor_tf_model.onnx.

Run:

python convert_to_onnx.py

---

Step 4: Test the ONNX Model

To verify the ONNX model, use ONNX Runtime for inference.

Create a new script named test_onnx.py and add:

import onnxruntime as ort
import numpy as np

# Load the ONNX model
session = ort.InferenceSession("xor_tf_model.onnx")

# Define XOR inputs
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
onnx_inputs = {session.get_inputs()[0].name: inputs}

# Get predictions
outputs = session.run(None, onnx_inputs)

# Print results
print("\nONNX Model Predictions:")
for i, inp in enumerate(inputs):
    print(f"Input: {inp} -> Prediction: {outputs[0][i][0]:.4f}")

Run:

python test_onnx.py

Expected Output:

ONNX Model Predictions:
Input: [0. 0.] -> Prediction: 0.02
Input: [0. 1.] -> Prediction: 0.98
Input: [1. 0.] -> Prediction: 0.98
Input: [1. 1.] -> Prediction: 0.02

This confirms that the ONNX model correctly predicts the XOR logic.

---

Conclusion

In this guide, we:

1. Trained an XOR neural network using TensorFlow.

2. Saved the trained model in TensorFlow format.

3. Converted the model to ONNX format using tf2onnx.

4. Tested the ONNX model using onnxruntime.

Now, the ONNX model is ready for cross-platform deployment and optimized inference. 🚀

4o

You said:

step-by-step instructions on how to load a ONNX xor model from TensorFlow and in test it using PyTorch

ChatGPT said:

Load an ONNX XOR Model from TensorFlow and Test It Using PyTorch

Lastly, his guide will walk you through how to:

1. Load a TensorFlow-trained XOR model saved in ONNX format

2. Test the ONNX model using PyTorch's inference capabilities

---

Step 1: Ensure Required Libraries Are Installed

Before proceeding, make sure you have the necessary dependencies installed:

pip install torch onnx onnxruntime numpy

• torch (PyTorch) → To use PyTorch for testing

• onnx → To work with ONNX models

• onnxruntime → To run ONNX inference

• numpy → For data handling

---

Step 2: Load and Test ONNX Model Using PyTorch

Since PyTorch does not natively execute ONNX models, we will use ONNX Runtime for inference.

2.1 Create a New Script test_onnx_with_pytorch.py

Create a new Python script and add the following code:

import onnx
import onnxruntime as ort
import numpy as np
import torch

# Step 1: Load the ONNX model
onnx_model_path = "xor_tf_model.onnx"
onnx_model = onnx.load(onnx_model_path)
onnx.checker.check_model(onnx_model)  # Validate the model
print("ONNX model loaded and validated successfully!")

# Step 2: Load ONNX Model Using ONNX Runtime
session = ort.InferenceSession(onnx_model_path)

# Step 3: Define XOR Test Inputs as PyTorch Tensor
inputs = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=torch.float32)

# Step 4: Convert PyTorch Tensor to NumPy for ONNX Runtime
onnx_inputs = {session.get_inputs()[0].name: inputs.numpy()}

# Step 5: Run the Model
outputs = session.run(None, onnx_inputs)

# Step 6: Convert Output to PyTorch Tensor
outputs_torch = torch.tensor(outputs[0])

# Step 7: Display Results
print("\nONNX Model Predictions Using PyTorch Tensor:")
for i, inp in enumerate(inputs):
    print(f"Input: {inp.numpy()} -> Prediction: {outputs_torch[i].item():.4f}")

---

Step 3: Run the Script and Test the Model

Run the script with:

python test_onnx_with_pytorch.py

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Expected Output

If everything is set up correctly, you should see output like this:

ONNX model loaded and validated successfully!

ONNX Model Predictions Using PyTorch Tensor:
Input: [0. 0.] -> Prediction: 0.02
Input: [0. 1.] -> Prediction: 0.98
Input: [1. 0.] -> Prediction: 0.98
Input: [1. 1.] -> Prediction: 0.02

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Conclusion

In this guide, we:

1. Loaded a TensorFlow-trained ONNX model.

2. Used ONNX Runtime to run inference.

3. Converted ONNX outputs to PyTorch tensors for compatibility.

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Load an ONNX XOR Model from PyTorch and Test It Using TensorFlow

This guide will walk you through:

1. Converting a trained PyTorch XOR model (saved in ONNX format) into TensorFlow format

2. Loading and testing the model using TensorFlow

---

Step 1: Install Required Libraries

Ensure you have the necessary dependencies installed:

pip install torch onnx onnxruntime tensorflow tf2onnx numpy

• torch (PyTorch) → For model creation and ONNX export

• onnx → For working with ONNX models

• onnxruntime → To validate the ONNX model

• tensorflow → To test the model in TensorFlow

• tf2onnx → To help with ONNX-TensorFlow conversion

• numpy → For handling data

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Step 2: Convert the ONNX Model to TensorFlow Format

Since TensorFlow does not natively run ONNX models, we need to convert the model first.

2.1 Create a Python Script convert_onnx_to_tf.py

Create a new script and add:

import onnx
import tf2onnx

# Step 1: Load the ONNX Model
onnx_model_path = "xor_pytorch_model.onnx"
onnx_model = onnx.load(onnx_model_path)

# Step 2: Convert ONNX Model to TensorFlow
tf_model_path = "xor_tf_model"
tf_rep, _ = tf2onnx.convert.from_onnx(onnx_model, output_path=tf_model_path)

print(f"\nModel successfully converted to TensorFlow format and saved as '{tf_model_path}'")

Breakdown of the Code

1. Load the ONNX model using onnx.load().

2. Convert the ONNX model to TensorFlow format using tf2onnx.convert.from_onnx().

3. Save the converted TensorFlow model to disk.

Run:

python convert_onnx_to_tf.py

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Step 3: Load and Test the TensorFlow Model

Now that we have a TensorFlow-compatible model, we can load and run inference.

3.1 Create a New Script test_xor_tf.py

Add the following code:

import tensorflow as tf
import numpy as np

# Step 1: Load the Converted TensorFlow Model
tf_model_path = "xor_tf_model"
model = tf.saved_model.load(tf_model_path)

# Step 2: Prepare XOR Test Inputs
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)

# Step 3: Run Model Inference
inference_func = model.signatures["serving_default"]
tf_inputs = tf.convert_to_tensor(inputs)
outputs = inference_func(tf_inputs)["output_0"]

# Step 4: Print Results
print("\nTensorFlow Model Predictions:")
for i, inp in enumerate(inputs):
    print(f"Input: {inp} -> Prediction: {outputs.numpy()[i][0]:.4f}")

Breakdown of the Code

1. Load the TensorFlow model using tf.saved_model.load().

2. Prepare XOR test inputs as a NumPy array.

3. Run inference using TensorFlow.

4. Print the predicted XOR outputs.

Run:

python test_xor_tf.py

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Expected Output

If everything is set up correctly, you should see:

TensorFlow Model Predictions:
Input: [0. 0.] -> Prediction: 0.02
Input: [0. 1.] -> Prediction: 0.98

Input: [1. 0.] -> Prediction: 0.98
Input: [1. 1.] -> Prediction: 0.02

---

Conclusion

In this guide, we:

1. Converted a PyTorch-trained XOR model (ONNX format) into TensorFlow format.

2. Loaded the TensorFlow model.

3. Tested the XOR model using TensorFlow.