Pytorch Geometric Tutorials

Overview

PytorchGeometricTutorial

Hi! We are Antonio Longa and Giovanni Pellegrini, PhD students, and PhD Gabriele Santin, researcher, working between Fondazione Bruno Kessler and the University of Trento, Italy.

This project aims to present through a series of tutorials various techniques in the field of Geometric Deep Learning, focusing on how they work and how to implement them using the Pytorch geometric library, an extension to Pytorch to deal with graphs and structured data, developed by @rusty1s.

You can find our video tutorials on Youtube and at our official website here.

Feel free to join our weekly online tutorial! For more details, have a look at the official website.

Tutorials:

Installation of PyG:

In order to have running notebooks in Colab, we use the following installation commands:

!pip install torch-scatter -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
!pip install torch-sparse -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
!pip install torch-geometric

These version are tested and running in Colab. If instead you run the notebooks on your machine, have a look at the PyG's installation instructions to find suitable versions.

Comments
  • DiffPool tutorial does not work

    DiffPool tutorial does not work

    Thank you for making the videos and notebooks available! They are very nice and helpful. I saw that the DiffPool model still does not work for the version that is uploaded here. I was wondering if you already have the working model available?

    Thank you in advance!

    opened by lisiq 4
  • Some tutorials no longer work with Google Colab

    Some tutorials no longer work with Google Colab

    Tutorial 14 and 15 both no longer work with colab and give this error after the second cell


    OSError Traceback (most recent call last) in () 2 import os 3 import pandas as pd ----> 4 from torch_geometric.data import InMemoryDataset, Data, download_url, extract_zip 5 from torch_geometric.utils.convert import to_networkx 6 import networkx as nx

    6 frames /usr/lib/python3.7/ctypes/init.py in init(self, name, mode, handle, use_errno, use_last_error) 362 363 if handle is None: --> 364 self._handle = _dlopen(self._name, mode) 365 else: 366 self._handle = handle

    OSError: /usr/local/lib/python3.7/dist-packages/torch_sparse/_convert_cpu.so: undefined symbol: _ZNK2at6Tensor5zero_Ev

    opened by itamblyn 2
  • Modify the example1

    Modify the example1

    https://github.com/AntonioLonga/PytorchGeometricTutorial/blob/main/Tutorial1/Tutorial1.ipynb

    I think this example could be modified for the better. In fact, the nums_layer = 1 parameter should be defined in Net, and a layer of GNNStack should be defined according to this parameter in the forward method. This would solve the problem raised by YouTube video 43:29.

    opened by abcdabcd989 2
  • Tutorial 3 code

    Tutorial 3 code

    Hi,

    Thanks for this great tutorials and videos. Really nice work.

    I was wondering about the GATLayer class in the code of tutorial 3. Once the class is made, it is no longer used after the 'Use it' heading in the notebook. Instead, the GATConv from torch geometric is used directly. Then why was the GATLayer class made?

    Thanks, VR

    opened by vandana-rajan 1
  • Error for running

    Error for running "from torch_geometric.nn import Node2Vec"

    while running from torch_geometric.nn import Node2Vec in google colab an error occur OSError: /usr/local/lib/python3.7/dist-packages/torch_sparse/_convert_cpu.so: undefined symbol: _ZNK2at6Tensor5zero_Ev

    what should I do?

    opened by ayreen2 1
  • Adding Colab support for the tutorials

    Adding Colab support for the tutorials

    Thanks for your effort and great work!

    I think, In order to make the tutorials more convenient for a wide audience it would be helpful to add a colab version of the notebooks with the special button, that redirects to the http://colab.research.google.com/.

    All tutorials can be run in colab via adding the notebook from GitHub and adding the cell with the installation of the pytorch-geometric and all dependencies. But the version with native support would make it more convenient.

    opened by Godofnothing 1
  • Question about Tutorial16.ipynb

    Question about Tutorial16.ipynb

    Hello, Thank you for the nice tutorial, it helps a lot to get started! I have a few questions concerning Tutorial16.ipynb: 1/ what is the effect of the parameter lin=True? 2/ what's the effect of changing the number of hidden and output channels? 3/ what is the purpose of l1, e1, l2, e2? Best, Claire

    opened by claireguepin 0
  • Some questions I found in this tutorial

    Some questions I found in this tutorial

    Hi, this is a nice tutorial. However, I find that there are some minor problems with the materials.

    1. I fond that they are same links so I think you can delete one. image
    2. In the node2vec practice colab notebook, the current installation requirement will lead the colab environment to break down. I tried this combination and it works: image Could you please figure out why? Thanks a lot!
    opened by HelloWorldLTY 0
Releases(v1.0.0)
Owner
Antonio Longa
Antonio Longa
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
Code for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning Pytorch Implementation for DisCo: Remedy Self-supervi

79 Jan 06, 2023
Classification Modeling: Probability of Default

Credit Risk Modeling in Python Introduction: If you've ever applied for a credit card or loan, you know that financial firms process your information

Aktham Momani 2 Nov 07, 2022
DeepMReye: magnetic resonance-based eye tracking using deep neural networks

DeepMReye: magnetic resonance-based eye tracking using deep neural networks

73 Dec 21, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022
AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

AttentionGAN-v2 for Unpaired Image-to-Image Translation AttentionGAN-v2 Framework The proposed generator learns both foreground and background attenti

Hao Tang 530 Dec 27, 2022
Sleep staging from ECG, assisted with EEG

Sleep_Staging_Knowledge Distillation This codebase implements knowledge distillation approach for ECG based sleep staging assisted by EEG based sleep

2 Dec 12, 2022
[NeurIPS-2021] Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Efficient Graph Similarity Computation - (EGSC) This repo contains the source code and dataset for our paper: Slow Learning and Fast Inference: Effici

23 Nov 11, 2022
YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitraril

Adam Van Etten 161 Jan 06, 2023
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
Implementation for Learning to Track with Object Permanence

Learning to Track with Object Permanence A video-based MOT approach capable of tracking through full occlusions: Learning to Track with Object Permane

Toyota Research Institute - Machine Learning 91 Jan 03, 2023
State-of-the-art data augmentation search algorithms in PyTorch

MuarAugment Description MuarAugment is a package providing the easiest way to a state-of-the-art data augmentation pipeline. How to use You can instal

43 Dec 12, 2022
Official repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment"

BasicVSR_PlusPlus (CVPR 2022) [Paper] [Project Page] [Code] This is the official repository for BasicVSR++. Please feel free to raise issue related to

Kelvin C.K. Chan 227 Jan 01, 2023
Trainable Bilateral Filter Layer (PyTorch)

Trainable Bilateral Filter Layer (PyTorch) This repository contains our GPU-accelerated trainable bilateral filter layer (three spatial and one range

FabianWagner 26 Dec 25, 2022
A SAT-based sudoku solver

SAT Sudoku solver A SAT-based Sudoku solver made in the context of a small project in the "Logic Problem Solving" class in the first year at the Polyt

Alexandre Malfreyt 5 Apr 15, 2022
Efficient Speech Processing Tookit for Automatic Speaker Recognition

Sugar Efficient Speech Processing Tookit for Automatic Speaker Recognition | HuggingFace | What's New EfficientTDNN: Efficient Architecture Search for

WangRui 14 Sep 14, 2022
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows"

OutliersSlidingWindows A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows" Dataset generatio

PaoloPellizzoni 0 Jan 05, 2022
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting Created by Yongming Rao*, Wenliang Zhao*, Guangyi Chen, Yansong Tang, Zheng Z

Yongming Rao 322 Dec 31, 2022
Deep Dual Consecutive Network for Human Pose Estimation (CVPR2021)

Beanie - is an asynchronous ODM for MongoDB, based on Motor and Pydantic. It uses an abstraction over Pydantic models and Motor collections to work wi

295 Dec 29, 2022