tensorflow-onnx - Accurate and Streamlined Model Transformation with tensorflow-onnx

Introduction to tf2onnx - Bridging the Gap Between TensorFlow and ONNX

Overview

The tf2onnx project serves as a vital tool for bridging the gap between various machine learning platforms and the ONNX (Open Neural Network Exchange) format. It provides the ability to convert models from TensorFlow versions 1.x and 2.x, Keras, Tensorflow.js, and Tflite directly into the ONNX format. This conversion can be achieved either via a command-line interface or through a Python API, broadening the accessibility and utility of machine learning models across different platforms.

Key Features

Multiple Input Supports: tf2onnx supports models developed in TensorFlow, Keras, TensorFlow.js, and Tflite. Recent updates have introduced experimental support for TensorFlow.js.
ONNX Opset Compatibility: It is compatible with ONNX opset versions from 14 to 18, using opset-15 as the default. This ensures that converted models maintain compatibility with a wide range of ONNX-supported environments.
Python and OS Compatibility: The tool supports Python versions 3.7 to 3.10 and is operable on both Linux and Windows systems, providing flexibility for developers working in different environments.

Installation and Setup

Prerequisites

Before installing tf2onnx, ensure that TensorFlow (either version 1.x or 2.x) is installed on your system. Additionally, if you intend to test the converted ONNX models, installing an ONNX runtime is advised. This can easily be done via pip:

pip install tensorflow
pip install onnxruntime

Installing tf2onnx

The installation of tf2onnx can be conducted from several sources:

From PyPI (Python Package Index):
```
pip install -U tf2onnx
```

From the Latest GitHub Repository:

pip install git+https://github.com/onnx/tensorflow-onnx

From Source Code: To keep up with the latest developments or contribute to the project, build from source:
```
git clone https://github.com/onnx/tensorflow-onnx
cd tensorflow-onnx
python setup.py install
```

Basic Usage

To convert a TensorFlow model to ONNX, the command-line interface provides a straightforward process:

For example, to convert a TensorFlow saved model:

python -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnx

For models stored in other formats, additional parameters (like input and output names) may be required.

Troubleshooting and Support

The project documentation includes detailed sections on the troubleshooting of common issues encountered during conversion. This is particularly useful given the complexity of model operations and their mappings between TensorFlow and ONNX.

Community and Contribution

tf2onnx is an open-source project, governed under the Apache 2.0 License, inviting contributions and improvements from the community. This collaborative ethos helps the tool evolve with new machine learning advancements and user needs.

Conclusion

By facilitating the conversion of TensorFlow and related models to ONNX, tf2onnx enhances the interoperability and deployment flexibility of machine learning applications. It empowers data scientists and engineers to leverage the strengths of various tools and frameworks, thus fostering innovation and the efficient deployment of AI solutions across platforms.