Paddle2ONNX: Bridging PaddlePaddle and ONNX
Introduction
Paddle2ONNX is an innovative tool designed to convert models developed in PaddlePaddle, a deep learning framework, into the Open Neural Network Exchange (ONNX) format. This conversion allows models to be deployed across a variety of inference engines such as TensorRT, OpenVINO, MNN, TNN, and NCNN, among others that support the ONNX open format. The result is increased flexibility and compatibility in deploying AI models in different environments.
Environment Dependencies
While Paddle2ONNX is self-contained and does not require additional components, it is recommended to be used in an environment with the following specifications:
- PaddlePaddle version 2.6.0
- ONNX runtime version 1.10.0 or higher
Installation Instructions
For those who wish to simply install Paddle2ONNX without further development needs, the installation can be accomplished quickly with the command:
pip install paddle2onnx
For developers who are interested in extending or modifying Paddle2ONNX, there is an option to compile it from the source code available on GitHub.
Quick Start Guide
Obtaining a PaddlePaddle Deployment Model
To successfully export models using Paddle2ONNX, users must work with a specific format that consists of two files:
model_name.pdmodel: This file defines the model structure.model_name.pdiparams: This file contains the model parameters.
Adjusting Paddle Model Configurations
If you need to modify the input or output parameters of a Paddle model, Paddle2ONNX provides resources through related tools. The necessary tutorials can be accessed for detailed instructions.
Converting PaddlePaddle Models Using Command Line
Paddle models can be converted to ONNX format through a simple command line operation:
paddle2onnx --model_dir model_dir \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file model.onnx
Numerous adjustable conversion parameters are available to tailor the process to your specific needs, such as model directory paths, version selections, and more.
ONNX Model Trimming and Optimization
When adjusting ONNX models is required, users should refer to applicable ONNX tools. For performance enhancements, the onnxslim tool comes highly recommended. It can optimize exported ONNX models effectively:
pip install onnxslim
onnxslim model.onnx slim.onnx
Contributing to Paddle2ONNX
The development of a robust ecosystem relies on collective collaboration. Developers keen on contributing to Paddle2ONNX can follow the provided development guide to lend their expertise and code to the project.
Licensing
The Paddle2ONNX tool is provided under the Apache-2.0 license, ensuring it is accessible for use and further development.
Acknowledgments
Paddle2ONNX owes its success to various contributors. Thanks are extended to PaddlePaddle for their continuous support, and to community members like chenwhql, luotao1, goocody, jeff41404, jzhang533, and ZhengBicheng for their generous donations which help drive the project's development forward.