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── 99288 repos indexed ── | filtered by tag: #geometric-deep-learning
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pyg-team/pytorch_geometric ★ 24024 4041 APPLICATION

PyTorch Geometric (PyG) is a library for Graph Neural Networks (GNNs) built on PyTorch. It provides a modular framework for building and training GNNs, covering the full pipeline from data handling to model evaluation. The core architecture includes a message-passing base class (MessagePassing) that underpins dozens of convolution layers (e.g., GCN, GAT, SAGE, GIN, PNA, GPS), a composable aggregation system (e.g., sum, mean, attention, set2set, transformers), pooling operators (e.g., TopK, SAGPool, DiffPool), normalization layers, and high-level model implementations (e.g., SchNet, DimeNet, LightGCN, TGN, SGFormer). The library supports heterogeneous graphs, temporal graphs, point clouds, knowledge graphs, and distributed training. Key entry points include the `torch_geometric.nn`, `torch_geometric.data`, `torch_geometric.loader`, `torch_geometric.datasets`, and `torch_geometric.transforms` modules. External dependencies are primarily PyTorch itself, with optional packages (torch-scatter, torch-sparse, pyg-lib, etc.) for optimized operations. The repository also contains extensive examples, benchmarks, and an experiment management system (GraphGym). Although primarily a software library, it serves as a research platform with many paper reproductions and dataset loaders, making it a hybrid between a production-grade library and a research codebase.

RATING: 9.9/10 MAINT: maintained DRIFT: low
#graph-neural-networks #pytorch #deep-learning #message-passing #library #gnn #geometric-deep-learning #graph-convolutional-networks
▸ 3 reusable components found
vLAR-group/GrowSP ★ 200 18 APPLICATION

GrowSP is an unsupervised semantic segmentation framework for 3D point clouds that learns from growing superpoints. The system processes raw point cloud data through preparation pipelines, generates initial superpoints using voxelization and region growing, then trains a Feature Pyramid Network (Res16FPN18) to learn discriminative features. During training, superpoints are progressively merged (growing superpoints strategy) to create hierarchical representations, with pseudo-labels generated via clustering of superpoint features. The framework includes specialized dataset classes for S3DIS, ScanNet, and SemanticKITTI, evaluation scripts using Hungarian matching for unsupervised performance measurement, and utility libraries for 3D augmentations and PLY file handling. The architecture follows a modular design with clear separation between data preparation, dataset handling, model definitions, and training/evaluation logic.

RATING: 7.9/10 MAINT: active DRIFT: medium
#unsupervised-seg #point-clouds #superpoints #growsp #minkowskiengine #fpn #pytorch #3d #3d-scene-understanding #3d-semantic-segmentation #geometric-deep-learning #pointcloud #self-driving #unsupervised-deep-learning #unsupervised-learning
▸ 4 reusable components found
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