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4 Commits (ee7bf687b274921c20ac1a86903defd2bb668c23)

Author	SHA1	Message	Date
mckay	a874ea78fb	feat: Enhance LossManager with advanced loss computation methods - Added `position_loss` method to calculate manifold loss using mean absolute value - Implemented `normals_loss` method to compute normal vector loss with gradient calculation - Updated `train.py` to log input and output tensor shapes during training - Modified network architecture in `train.py` by increasing hidden layer dimensions from [64, 64, 64] to [256, 256, 256]	4 weeks ago
mckay	6be600a52f	feat: Add LossManager for manifold loss computation - Created `loss.py` with a `LossManager` class to handle loss calculation - Integrated `LossManager` into the training pipeline in `train.py` - Implemented a basic manifold loss computation using mean absolute value	4 weeks ago
mckay	a40fb611d5	feat: Implement NHREP Dataset and Training Pipeline - Added `data_loader.py` with `NHREP_Dataset` class for loading point cloud, feature mask, and CSG tree data - Implemented `CustomDataLoader` for flexible data loading with configurable parameters - Refactored `train.py` to create a structured training pipeline for NHRepNet - Added support for feature sampling, device selection, and TensorBoard logging - Introduced modular training methods with error handling and logging	4 weeks ago
mckay	14674e1a80	refactor: simplest trrain loss	4 weeks ago