sgas
pytorch_geometric
| sgas | pytorch_geometric | |
|---|---|---|
| 3 | 9 | |
| 160 | 20,154 | |
| - | 1.1% | |
| 0.0 | 9.8 | |
| almost 3 years ago | 1 day 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.
sgas
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
What are some alternatives?
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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
GeometricFlux.jl - Geometric Deep Learning for Flux
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
graph_nets - Build Graph Nets in Tensorflow
sr-drl - Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks
gym - A toolkit for developing and comparing reinforcement learning algorithms.
GNNs-Recipe - 🟠A study guide to learn about Graph Neural Networks (GNNs)
GraphNorm - [ICML 2021] GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training (official implementation)