{"id":13869914,"url":"https://github.com/tchaton/lightning-geometric","last_synced_at":"2025-10-08T07:37:01.749Z","repository":{"id":40605903,"uuid":"294764248","full_name":"tchaton/lightning-geometric","owner":"tchaton","description":"Integrate pytorch","archived":false,"fork":false,"pushed_at":"2021-04-04T09:23:50.000Z","size":1854,"stargazers_count":46,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-11T21:48:23.875Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tchaton.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-09-11T17:34:52.000Z","updated_at":"2025-01-10T00:04:48.000Z","dependencies_parsed_at":"2022-09-04T11:21:05.529Z","dependency_job_id":null,"html_url":"https://github.com/tchaton/lightning-geometric","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tchaton/lightning-geometric","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tchaton%2Flightning-geometric","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tchaton%2Flightning-geometric/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tchaton%2Flightning-geometric/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tchaton%2Flightning-geometric/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tchaton","download_url":"https://codeload.github.com/tchaton/lightning-geometric/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tchaton%2Flightning-geometric/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278909713,"owners_count":26066887,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-05T20:01:21.728Z","updated_at":"2025-10-08T07:37:01.731Z","avatar_url":"https://github.com/tchaton.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# TorchScripted Pytorch Geometric Examples with Pytorch Lightning and Hydra\n\n[![codecov](https://codecov.io/gh/tchaton/lightning-geometric/branch/master/graph/badge.svg)](https://codecov.io/gh/tchaton/lightning-geometric) [![Actions Status](https://github.com/tchaton/lightning-geometric/workflows/unittest/badge.svg)](https://github.com/tchaton/lightning-geometric/actions)\n\n### Setup on MacOs. Please, adapt to others OS :)\n\n```python\nbrew install cmake\npyenv install 3.7.8\npyenv local 3.7.8\npython -m venv\nsource .venv/bin/activate\npoetry install\n```\n\n### PRINCIPAL CMD\n\n```python\npython train.py model={{MODEL}} dataset={{DATASET}} loggers={{LOGGERS}} log={{LOG}} notes={{NOTES}} name={{NAME}} jit={{JIT}}\n```\n\n- `LOGGERS` str: Configuration file to log to Wandb, currently using mine as `thomas-chaton`\n- `LOG` bool: Wheter to log training to wandb\n- `NOTES` str: A note associated to the training\n- `NAME` str: Training name appearing on Wandb.\n- `LOG` bool: Wheter to make model jittable.\n\n### Working Inference\n\nHave a look at `test/test_inference.py`\n\n### SUPPORTED COMBINAISONS\n\n| `{{DATASET}}` | `{{MODEL}}` | DATASET DESCRIPTION                                                                                                                                                                       | MODEL DESCRIPTION                                                                                                                                                                          | WORKING                      |     |\n| ------------- | ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------- | --- |\n| zinc          | pna         | The ZINC dataset from the \"Grammar Variational Autoencoder\" \u003chttps://arxiv.org/abs/1703.01925\u003e                                                                                            | The Principal Neighbourhood Aggregation graph convolution operator from the \"Principal Neighbourhood Aggregation for Graph Nets\" \u003chttps://arxiv.org/abs/2004.05718\u003e                        | True                         |\n| faust         | spline      | The FAUST humans dataset from the \"FAUST: Dataset and Evaluation for 3D Mesh Registration\" \u003chttp://files.is.tue.mpg.de/black/papers/FAUST2014.pdf\u003e                                        | The spline-based convolutional operator from the \"SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels\"\u003chttps://arxiv.org/abs/1711.08920\u003e                              | In progress                  |\n| ppi           | gat         | The protein-protein interaction networks from the \"Predicting Multicellular Function through Multi-layer Tissue Networks\" \u003chttps://arxiv.org/abs/1707.04638\u003e                              | The graph attentional operator from the \"Graph Attention Networks\" \u003chttps://arxiv.org/abs/1710.10903\u003e True                                                                                 | True                         |\n| cora          | agnn        | The citation network datasets \"Cora\", \"CiteSeer\" and \"PubMed\" from the \"Revisiting Semi-Supervised Learning with Graph Embeddings\" \u003chttps://arxiv.org/abs/1603.08861\u003e                     | \"Attention-based Graph Neural Network for Semi-Supervised Learning\" \u003chttps://arxiv.org/abs/1803.03735\u003e                                                                                     | True                         |\n| cora          | sage        | \"\"                                                                                                                                                                                        | The GraphSAGE operator from the \"Inductive Representation Learning on Large Graphs\" \u003chttps://arxiv.org/abs/1706.02216\u003e                                                                     | True                         |\n| cora          | sgc         | \"\"                                                                                                                                                                                        | The simple graph convolutional operator from the \"Simplifying Graph Convolutional Networks\" \u003chttps://arxiv.org/abs/1902.07153\u003e                                                             | True                         |\n| cora          | tag         | \"\"                                                                                                                                                                                        | The topology adaptive graph convolutional networks operator from the \"Topology Adaptive Graph Convolutional Networks\" \u003chttps://arxiv.org/abs/1710.10370\u003e                                   | True                         |\n| cora          | dna         | \"\"                                                                                                                                                                                        | The dynamic neighborhood aggregation operator from the \"Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks\" \u003chttps://arxiv.org/abs/1904.04849\u003e                   | True                         |\n| reddit        | sage        | The Reddit dataset from the \"Inductive Representation Learning on Large Graphs\" \u003chttps://arxiv.org/abs/1706.02216\u003e                                                                        | \"\"                                                                                                                                                                                         | True                         |\n| reddit        | agnn        | \"\"                                                                                                                                                                                        | \"\"                                                                                                                                                                                         | True                         |\n| icews18       | renet       | The Integrated Crisis Early Warning System (ICEWS) dataset used in the, _e.g._, \"Recurrent Event Network for Reasoning over Temporal Knowledge Graphs\" \u003chttps://arxiv.org/abs/1904.05530\u003e | The Recurrent Event Network model from the \"Recurrent Event Network for Reasoning over Temporal Knowledge Graphs\" \u003chttps://arxiv.org/abs/1904.05530\u003e                                       | Waiting for support for TGCN |\n| cora          | argva       | \"\"                                                                                                                                                                                        | The Adversarially Regularized Variational Graph Auto-Encoder model from the \"Adversarially Regularized Graph Autoencoder for Graph Embedding\" \u003chttps://arxiv.org/abs/1802.04407\u003e`          | True                         |\n| cora          | arma        | \"\"                                                                                                                                                                                        | The ARMA graph convolutional operator from the \"Graph Neural Networks with Convolutional ARMA Filters\" \u003chttps://arxiv.org/abs/1901\u003e.01343\u003e                                                 | True                         |\n| cora          | gcn         | \"\"                                                                                                                                                                                        | The GCN graph convolutional operator from the \"Semi Supervised Classification with Graph Convolution Networks\" \u003chttps://arxiv.org/pdf/1609.02907.pdf\u003e.01343\u003e                               | True                         |\n| cora          | gcn2        | \"\"                                                                                                                                                                                        | The graph convolutional operator with initial residual connections and identity mapping (GCNII) from the \"Simple and Deep Graph Convolutional Networks\" \u003chttps://arxiv.org/abs/2007.02133\u003e | True                         |\n\n# DATASET SIZES\n\n```\n529M    ./Flickr\n 74M    ./FAUST\n 16M    ./cora\n3.5G    ./Reddit\n383M    ./ZINC\n1.8G    ./MNISTSuperpixels\n182M    ./OgbnArxiv\n192M    ./PPI\n156M    ./ICEWS18\n6.8G    .\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftchaton%2Flightning-geometric","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftchaton%2Flightning-geometric","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftchaton%2Flightning-geometric/lists"}