pytorch_geometric
deep_gcns_torch
| pytorch_geometric | deep_gcns_torch | |
|---|---|---|
| 9 | 1 | |
| 20,154 | 1,118 | |
| 1.3% | - | |
| 9.8 | 0.0 | |
| 3 days ago | almost 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pytorch_geometric
- Please help I'm suffering | RuntimeError: mat1 and mat2 must have the same dtype
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Looking for Point Cloud deep learning, training sources
https://github.com/pyg-team/pytorch_geometric/tree/master/examples any of the scripts ending in _segmentation.py can be used for semantic segmentation of point clouds
- Why is the loss not decreasing with Pytorch Geometric GATv2Conv (and GATconv) ??
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MetaPath2Vec from Pytorch geometric with HeteroData Dataset
Here the code I'm referring to: https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/metapath2vec.py
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[N] PyG 2.3.0 released: PyTorch 2.0 support, native sparse tensor support, explainability and accelerations
Today version 2.3 got released: https://github.com/pyg-team/pytorch_geometric/releases/tag/2.3.0
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Custom Point Cloud semantic segmentation
Here is a working example for semantic segmentation with pointnet++ using PyTorch geometric. There are equivalent scripts for dgcnn, randlanet, point transformer in the same folder https://github.com/pyg-team/pytorch_geometric/blob/master/examples/pointnet2_segmentation.py
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RL Agent Library to use graph in spaces
I don't know if any RL library includes an already implemented agent that can process graphs. However there are a number of deep learning frameworks that can help with the implementation of graph neural networks, especially Graph Nets (based on Tensorflow) and PyTorch Geometric. You might need to modify an existing RL agent to make use of one of these frameworks. If you are not familiar with GNNs you can look up these surveys. This article may also be of interest to you: it tackles graph-based environments, and the paper's code is available (it has a custom implementation of A2C and uses PyTorch Geometric -- btw it doesn't use Gym's space.graph since this feature is very recent in Gym).
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TensorFlow Graph Neural Networks
Meanwhile, PyTorch-Geometric is 3 years old and 13K stars on Github.
https://github.com/pyg-team/pytorch_geometric
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[D] Asking for ideas on Matrix of only 0/1 values as inputs and real value outputs
A pytorch implementation: https://github.com/rusty1s/pytorch_geometric
deep_gcns_torch
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[R] Graph Convolutional Networks in Videos and 3D Point Clouds - Dr. Ali Thabet - Link to free zoom lecture by the author in comments
DeepGCNs: Can GCNs Go as Deep as CNNs? (ICCV 2019) Paper page: https://www.deepgcns.org/ Git: https://github.com/lightaime/deep_gcns_torch
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
GraphMixerNetworks - Official Implementation of Graph Mixer Networks
GeometricFlux.jl - Geometric Deep Learning for Flux
graphlearn-for-pytorch - A GPU-accelerated graph learning library for PyTorch, facilitating the scaling of GNN training and inference.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Ringer-Client - Ringer is a new messaging app for windows. Its currently in beta but lets work together to make it better. The code here is snapshot code so it might not work as intended. To get the latest stable build pls download the installer.
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
Bi-Real-net - Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. In ECCV 2018 and IJCV
sgas - SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas
nasa_impacts - Plotly visualizations of NASA's IMPACTS snow storm study program