pytorch_geometric
Graph Neural Network Library for PyTorch (by pyg-team)
sr-drl
Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks (by jaromiru)
| pytorch_geometric | sr-drl | |
|---|---|---|
| 9 | 1 | |
| 20,154 | 23 | |
| 1.3% | - | |
| 9.8 | 4.0 | |
| 5 days ago | 9 months ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of pytorch_geometric.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-13.
- 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
sr-drl
Posts with mentions or reviews of sr-drl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-22.
-
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).
What are some alternatives?
When comparing pytorch_geometric and sr-drl you can also consider the following projects:
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
graph_nets - Build Graph Nets in Tensorflow
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]
sgas - SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas
GNNs-Recipe - 🟠A study guide to learn about Graph Neural Networks (GNNs)
pytorch_geometric vs dgl
sr-drl vs gym
pytorch_geometric vs gnn-lspe
sr-drl vs graph_nets
pytorch_geometric vs GeometricFlux.jl
pytorch_geometric vs pytorch_geometric_temporal
pytorch_geometric vs deep_gcns_torch
pytorch_geometric vs pytorch_geometric
pytorch_geometric vs sgas
pytorch_geometric vs graph_nets
pytorch_geometric vs gym
pytorch_geometric vs GNNs-Recipe