.cursorrules

I have thousands of 3D objects in various formats of meshes, level of detail, formats etc. I want to use pytorch to train a model to convert a high fidelity mesh to a lower level of detail, with minimal loss.

We will work in an iterative manner, please try to follow these instructions and expectations to our process.

0. "The combined_output.txt holds code for the current code-base, of the important components. Take the current implementation into account when suggesting changes. Especially when a change in one file leads to a need to change another class or function. Suggesting new content for an existing class, should consider the current implementation and not just burst out based on assumptions what it should be."

1. "Analyze research papers thoroughly, breaking down complex concepts into clear, step-by-step explanations. Focus on architectural details, mathematical formulations, and key algorithms."

2. "When implementing new components, start with a high-level class structure. Outline necessary imports, initialization methods, and key function signatures before diving into detailed implementations."

3. "For each significant component or method, suggest comprehensive unit tests. Cover normal operations, edge cases, and potential failure modes."

4. "When addressing code errors, provide a detailed analysis of the error message and traceback. Explain the root cause and reasoning behind proposed solutions."

5. "Maintain consistency with the original research paper or design document. Regularly cross-reference implementations against the source material."

6. "Consider compatibility with relevant libraries and frameworks (e.g., PyTorch, PyTorch Geometric) when suggesting code implementations."

7. "Offer clear, intuitive explanations for complex mathematical concepts, especially those central to the project's core algorithms."

8. "Suggest visualization techniques for intermediate results to aid in debugging and understanding model behavior."

9. "Keep track of the overall project structure. Recommend refactoring or reorganization to maintain clean, modular, and efficient code."

10. "When implementing loss functions or evaluation metrics, explain the intuition and mathematical basis behind each component."

11. "Provide guidance on performance optimization, including suggestions for efficient data handling, model architecture improvements, and computational optimizations."

12. "Offer insights on potential extensions or modifications to the current project, based on recent advancements in the field or related research."

13. "When discussing implementation details, consider scalability and potential deployment scenarios."

14. "Suggest best practices for documentation, function docstrings, and broader project documentation."

15. "Provide guidance on experiment design, including ablation studies and comparative analyses with baseline methods."

16. "For the code generated, please do not add trivial comments or code comments that are instructions of what to change compared to current code"