- Modified default `exc_info` parameter to `False` in logger's error method
- Changed default file log level from DEBUG to INFO in LogConfig
- Improved default error logging behavior to reduce unnecessary stack trace output
- Added comprehensive inline comments explaining each step of the training process in the `run_nhrepnet_training` method
- Improved code structure by adding descriptive comments for variable initializations and key computational steps
- Enhanced code readability by breaking down complex operations with clear explanatory comments
- Maintained existing functionality while providing better code documentation
- Replaced print statements with logger.info() in ReconstructionRunner class
- Added logging for input and output tensor shapes in NHRepNet forward method
- Improved logging consistency and added docstring for network forward method
- Introduced a new command-line argument `--csv_name` to allow custom CSV filename for evaluation results
- Updated `compute_all()` function to use the dynamically specified CSV filename
- Improved flexibility of evaluation script by enabling users to specify output file name
- Maintained existing logging and error handling mechanisms
- Added detailed comments to clarify the steps involved in the normalization of mesh vertices.
- Implemented calculations for the bounding box size and center point of the model.
- Updated the vertex normalization logic to center and scale vertices to the range of [-0.9, 0.9].
- Improved code readability and maintainability by providing clear explanations for each processing step.
- Introduced `FeatureSampleConfig` class to encapsulate configuration parameters for feature sampling from command line arguments.
- Implemented methods for parsing and validating input parameters, ensuring required fields are provided.
- Updated `Source.cpp` to utilize the new configuration class, streamlining command line argument handling and improving code readability.
- Enhanced error handling for missing or invalid parameters, promoting robustness in the feature sampling process.
- Expanded the research sections in idea.md to include new innovation points and expected outcomes for noise robustness and feature preservation.
- Introduced a Dockerfile for setting up the pre-processing environment with necessary packages.
- Added a .gitignore file to exclude training data and raw input directories from version control.
- Updated README.md with clearer instructions and code formatting for better usability.
- Introduced a new function `load_and_process_single_model` to encapsulate the logic for evaluating a single model, enhancing code readability and maintainability.
- Updated `compute_all` to utilize the new function, streamlining the overall evaluation workflow.
- Improved error handling with logging for missing files and exceptions during processing.
- Enhanced caching mechanism for computed results to avoid redundant calculations.
- Added detailed comments and documentation for better understanding of the evaluation process.
- Changed the permissions of build.sh to make it executable.
- Simplified the cmake command in build.sh by removing hardcoded paths for CUDNN.
- Updated CMakeLists.txt in console_pytorch and evaluation directories to reflect a new CMAKE_PREFIX_PATH for libtorch, ensuring compatibility with the current workspace structure.
- Added project root directory setup to ensure consistent file paths.
- Integrated a custom logger for enhanced logging capabilities.
- Updated argument parsing to use absolute paths for ground truth and prediction data.
- Improved documentation for distance functions and added a new function to compute feature distances and angle differences.
- Refactored file reading to use context management for better resource handling.
- Split the initialization process into multiple private methods for better readability and maintainability.
- Added detailed logging for each step of the initialization process, including error handling for missing parameters and file loading issues.
- Enhanced configuration and directory setup with clearer error messages and structured logging.
- Improved data loading methods to handle both single and list-based data inputs more robustly.
- Introduced methods for setting up the CSG tree and computing local sigma values, with appropriate logging for each operation.
- Introduced a `Logger` class that implements a singleton pattern for logging.
- Added a `ColoredFormatter` to provide colored log output based on log levels.
- Implemented methods for logging at different levels (debug, info, warning, error, exception).
- Included functionality to capture caller information and log it alongside messages.
- Created a `LogConfig` dataclass for easy configuration of logging parameters.
- Set up a global logger instance with default configuration.
- Added a `timeit` decorator for measuring function execution time.
mckay