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
During February 2025, Zhang Honggeng enhanced the PaddlePaddle/Paddle2ONNX repository by expanding ONNX export capabilities and improving model interoperability. He implemented features such as decomposing PIR program support and loading pretrained parameters, addressing real-world model translation needs. Using C++ and Python, Zhang focused on operator mapping, numerical correctness, and 3D convolution support, ensuring accurate model conversion and inference. His work included targeted bug fixes, code maintenance, and test automation to strengthen reliability and reduce regressions. By refining parameter handling and adding new operator support, Zhang delivered robust solutions that improved production stability and accelerated future development within the project.
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
108 people
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
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