Graph-Convolution-on-Structured-Documents
pytorch_geometric
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| Graph-Convolution-on-Structured-Documents | pytorch_geometric | |
|---|---|---|
| 1 | 9 | |
| 141 | 20,110 | |
| - | 2.2% | |
| 0.0 | 9.8 | |
| over 1 year ago | 6 days ago | |
| Python | Python | |
| - | MIT License |
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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.
Graph-Convolution-on-Structured-Documents
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Tips on running github projects without specific setup instructions?
I am trying to run this python project but I have hard time even installing requirements.txt And actually I don't have any more ideas what to do to run in properly. I can imagine that situation like this one happens more often and I would like to receive some tips on what to do in such situations in general.
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?
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
jina - ☁️ Build multimodal AI applications with cloud-native stack
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
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
graph_nets - Build Graph Nets in Tensorflow
sr-drl - Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks