Project Overview: onnx2tf
onnx2tf is a specialized tool designed to facilitate the conversion of ONNX (Open Neural Network Exchange) files, typically using the NCHW data format, into formats compatible with TensorFlow, TFLite, or Keras, which generally use the NHWC data format. This tool addresses a specific challenge in the realm of machine learning model conversion -- the problem of excessive Transpose operations that occur when using onnx-tensorflow, known as onnx-tf.
Purpose and Development
The primary goal of onnx2tf is to streamline and optimize the process of converting deep learning models from ONNX to TensorFlow-related formats. It aims to tackle and simplify the often cumbersome procedure of handling Transpose operations automatically, thus accelerating model deployment in various environments.
Project Dynamics
The tool is continually evolving, with ongoing efforts to introduce advanced model optimizations and address bugs. Consequently, new users might occasionally encounter bugs that bypass the continuous integration regression testing. As a solution, users are advised to experiment with different package versions if they face any issues.
Despite being the creator, the developer mentions never having used onnx2tf in any practical scenario, reflecting a focus on development and optimization over personal use.
Key Advantages
One of the notable shifts in the tool’s landscape is the introduction of torch.onnx.dynamo_export, a PyTorch feature that enhances the ONNX conversion process. This new approach, available from PyTorch v2.2.0, provides better alignment with PyTorch implementations, facilitating the conversion of previously challenging operations into ONNX format. This change is poised to promote model versatility in the future.
Additional Integrations
- AI Edge Torch: Developed by Google, this library supports transforming PyTorch models into .tflite files, thereby enabling the seamless execution of these models on mobile and IoT devices.
- nobuco: Known for its Pythonic, efficient integration with TensorFlow/Keras, nobuco is recommended for future-oriented model conversion paths.
Supported Layers and Operations
onnx2tf supports a comprehensive array of ONNX operations, with many being fully supported, while others may require further development effort. The tool is open to contributions, and users are encouraged to submit pull requests for expanding functionality.
Environment Compatibility
onnx2tf works seamlessly across multiple operating systems, including Linux and Windows, and requires specific version alignments with dependencies like TensorFlow, ONNX, and others.
Installation and Usage
The tool can be installed via Docker or directly using pip, depending on user preference and environment configuration. Detailed installation guides are available for both local environments and cloud-based platforms like Google Colaboratory.
Conclusion
In essence, onnx2tf fills a crucial role in the deep learning ecosystem by offering a streamlined pathway for converting machine learning models across different frameworks, thereby overcoming specific limitations found in existing tools. With a flexible foundation and active development, it supports a wide range of operations crucial for effective model deployment in real-world scenarios.