PaddlePaddle/Paddle2ONNX
Feb 2025 – Feb 2025
1 Month active
Languages Used
BatchBatch ScriptC++PythonShellShell Script
Technical Skills
API DesignC++C++ DevelopmentCI/CDCode CleanupCode Maintenance
zhanghonggeng
PROFILE
Zhanghonggeng
Worked on the PaddlePaddle/Paddle2ONNX repository to expand ONNX export capabilities, focusing on accurate model translation and improved runtime behavior. Enhanced operator coverage, parameter handling, and numerical operations, including support for 3D convolutions and advanced checks like ISINITE, ISINF, and ISNAN. Used C++ and Python to implement new features, address bugs, and strengthen test automation, ensuring reliability across common workflows. Prioritized code maintenance and refactoring to reduce regressions and accelerate future development. The work enabled more robust model conversion and inference, supporting real-world architectures and production stability through targeted updates, debugging, and continuous integration practices.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
25Total
Bugs
6
Commits
25
Features
13
Lines of code
2,907
Activity Months1
Your Network
116 peopleShared Repositories
9
Work History
February 2025
25 Commits • 13 Features
Feb 1, 2025February 2025 performance summary for PaddlePaddle/Paddle2ONNX focused on expanding ONNX export capabilities, improving numerical correctness, and strengthening reliability across common workflows. Delivered 5 key features with supporting tests and targeted bug fixes to enhance interoperability, model transferability, and stability in production. Key focus areas: - Expanded operator coverage and correctness in ONNX export, enabling more accurate model translation and runtime behavior. - Improved parameter handling, numerical operations, and 3D convolution support to align with real-world model architectures. - Strengthened testing and maintenance to reduce regressions and accelerate future iterations.
25 Commits • 13 Features
Feb 1, 2025February 2025 performance summary for PaddlePaddle/Paddle2ONNX focused on expanding ONNX export capabilities, improving numerical correctness, and strengthening reliability across common workflows. Delivered 5 key features with supporting tests and targeted bug fixes to enhance interoperability, model transferability, and stability in production. Key focus areas: - Expanded operator coverage and correctness in ONNX export, enabling more accurate model translation and runtime behavior. - Improved parameter handling, numerical operations, and 3D convolution support to align with real-world model architectures. - Strengthened testing and maintenance to reduce regressions and accelerate future iterations.
February 2025
Activity
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Quality Metrics
Correctness82.4%
Maintainability83.2%
Architecture79.2%
Performance70.4%
AI Usage20.8%
Skills & Technologies
Programming Languages
BatchBatch ScriptC++PythonShellShell Script
Technical Skills
API DesignC++C++ DevelopmentCI/CDCode CleanupCode MaintenanceCode RefactoringCommand-line InterfaceDebuggingDeep LearningLoggingMachine LearningModel ConversionModel InferenceONNX
Repositories Contributed To
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