Claude/standalone implementation setup 01 bq mf5 x wm l8y fx ju h dcvd z9 - #29
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This commit introduces a clean interface for the SSSD-ECG diffusion model: New Features: - model_wrapper.py: SSSDECG class with simplified API - model(x, y) for training loss computation - model.generate() for sample generation - Built-in checkpoint save/load functionality - dataset.py: Dedicated Dataset classes - ECGDataset: Generic ECG dataset class - PTBXLDataset: PTB-XL specific dataset - create_dataloaders(): Utility function for easy DataLoader creation - train_new.py: Clean training script using new interface - Progress tracking with tqdm - Flexible configuration via command line or JSON - Better checkpoint management - example_usage.py: Comprehensive usage examples - Basic training loop - Sample generation - Checkpoint operations - Real data handling - README_REFACTORED.md: Japanese documentation - API reference - Usage examples - Troubleshooting guide Benefits: - Cleaner separation of concerns (data, model, training) - Easier to use and extend - Better code organization - Original implementation remains unchanged
…ader-01FjNF9yHomAMfj5bjHrSeNF Refactor SSSD-ECG: Separate data loading from model implementation
Create a standalone, self-contained implementation of SSSD-ECG that can be easily copied to other projects. Structure: - sssd_standalone/: Main package directory - model_wrapper.py: Clean API wrapper for SSSDECG model - dataset.py: Dataset classes (ECGDataset, PTBXLDataset) - models/: Model implementations (SSSD_ECG, S4Model) - utils/: Utility functions for diffusion, training, sampling - config/: Configuration file (config_SSSD_ECG.json) - examples/: Training and usage examples - README.md: Comprehensive documentation - requirements.txt: Dependencies Features: - Simple API for training and inference - Modular design with separated components - Well-documented with usage examples - Easy to integrate into other projects - Includes parameter configuration file
- Replace pytorch_lightning.utilities.rank_zero_only with simple local implementation - Add missing dependencies (einops, opt_einsum) to requirements.txt - This makes the standalone implementation truly independent without heavy dependencies The rank_zero_only decorator is now a simple pass-through function for standalone usage. For distributed training, users can modify this decorator as needed.
- Add setup.py for pip installation support - Add MANIFEST.in for package distribution - Add example_external_usage.py demonstrating 3 import methods - Update README with detailed installation instructions Users can now: 1. Install as package: pip install -e . 2. Use sys.path in external projects 3. Set PYTHONPATH environment variable This makes sssd_standalone easier to integrate into other projects.
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