Are you looking to convert your TensorFlow, Keras, TensorFlow.js, and TFLite models into ONNX format? Whether you’re aiming for portability across platforms or performance gains with ONNX-compatible runtimes, the tf2onnx tool is here to make your life easier! In this guide, we’ll walk you through the conversion process and troubleshoot any potential issues.

What is tf2onnx?

tf2onnx is a command-line tool and Python API that allows for the conversion of TensorFlow models to ONNX format, facilitating model interoperability between different frameworks. It’s compatible with TensorFlow versions ranging from 1.x to 2.x and has recently added support for TensorFlow.js models. Additionally, it supports TFLite models!

Getting Started with tf2onnx

To kick off your conversion journey, follow these steps:

• Install TensorFlow: If you haven’t already, install the desired TensorFlow version. You can do this by executing:

pip install tensorflow

• (Optional) Install ONNX Runtime: This is necessary for running tests:

pip install onnxruntime

• Install tf2onnx: You can install it using pip:

pip install -U tf2onnx

• Convert your model: Run the following command to convert your model:

python -m tf2onnx.convert --saved-model PATH_TO_TENSORFLOW_MODEL --output OUTPUT_MODEL.onnx

Understanding the Conversion Process:

Imagine you’re moving houses. Converting a TensorFlow model to ONNX is akin to packing your belongings, where:

• TensorFlow models: are your items that need packing.
• ONNX format: is the new house where you’ll unpack your items.
• Boxing up (Conversion): is carefully ensuring that each item (layer and operation) is securely packed in a way that they can seamlessly fit into your new home.

In more technical terms, tf2onnx takes care of mapping TensorFlow operations to their ONNX equivalents, managing input/output data formats, and optimizing the resultant ONNX model for effective performance.

Troubleshooting Common Errors

Even the best plans can face hiccups. Here are some frequent issues you might encounter:

• Unsupported Ops: If you run into unsupported TensorFlow operations, consult the supported ops list for reference.
• Errors with Input/Output: Ensure your input and output node names are accurately specified; if uncertain, utilize the summarize_graph tool to locate them.
• When in doubt: Sometimes specifying an older or newer ONNX opset via the --opset command may resolve issues.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Happy converting! May your models always run smoothly!