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https://github.com/xiaoxinhe/graph-vit-mlpmixer
Official Implementation of ICML 2023 paper: "A Generalization of ViT/MLP-Mixer to Graphs"
https://github.com/xiaoxinhe/graph-vit-mlpmixer
icml-2023
Last synced: 7 days ago
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Official Implementation of ICML 2023 paper: "A Generalization of ViT/MLP-Mixer to Graphs"
- Host: GitHub
- URL: https://github.com/xiaoxinhe/graph-vit-mlpmixer
- Owner: XiaoxinHe
- License: mit
- Created: 2022-10-05T03:15:09.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-22T04:02:48.000Z (7 months ago)
- Last Synced: 2024-04-22T06:24:34.981Z (7 months ago)
- Topics: icml-2023
- Language: Python
- Homepage: https://arxiv.org/abs/2212.13350
- Size: 314 KB
- Stars: 136
- Watchers: 3
- Forks: 19
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: license.md
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README
# Graph-ViT-MLPMixer
[](https://arxiv.org/abs/2212.13350) [](https://paperswithcode.com/sota/graph-classification-on-peptides-func?p=a-generalization-of-vit-mlp-mixer-to-graphs) [](https://paperswithcode.com/sota/graph-regression-on-peptides-struct?p=a-generalization-of-vit-mlp-mixer-to-graphs) [](https://paperswithcode.com/sota/graph-regression-on-zinc?p=a-generalization-of-vit-mlp-mixer-to-graphs)
# Citation
```
@inproceedings{he2023generalization,
title={A generalization of vit/mlp-mixer to graphs},
author={He, Xiaoxin and Hooi, Bryan and Laurent, Thomas and Perold, Adam and LeCun, Yann and Bresson, Xavier},
booktitle={International Conference on Machine Learning},
pages={12724--12745},
year={2023},
organization={PMLR}
}
```
# Python environment setup with Conda
```
conda create --name graph_mlpmixer python=3.8
conda activate graph_mlpmixer
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
conda install -c pyg pytorch-sparse
conda install -c pyg pytorch-scatter
conda install -c pyg pytorch-cluster
conda install -c pyg pyg
pip install ogb
conda install -c conda-forge rdkit
pip install yacs
pip install tensorboard
pip install networkx
pip install einops
# METIS
conda install -c conda-forge metis
pip install metis
```
# Run Graph MLPMixer
## Run different datasets
See all available datasets under the `train/` folder.
```
# Running Graph MLPMixer on simulation datasets
python -m train.exp
# Running Graph MLPMixer for ZINC
python -m train.zinc
# Running Graph MLPMixer for CIFAR10
python -m train.cifar10
# Running Graph MLPMixer on OGB datasets
python -m train.molhiv
# Running Graph MLPMixer on LRGB datasets
python -m train.peptides_func
```
## Run different base MP-GNNs
See all available base MP-GNNs in `core/model_utils/pyg_gnn_wrapper.py`.
```
python -m train.zinc model.gnn_type GCNConv
python -m train.zinc model.gnn_type ResGatedGraphConv
...
```
## Run ablation studies
See `core/config.py` for all options.
```
# Running Graph MLPMixer w/o NodePE
python -m train.zinc pos_enc.rw_dim 0 pos_enc.lap_dim 0
# Running Graph MLPMixer w/o PatchPE
python -m train.zinc model.patch_rw_dim 0
# Running Graph MLPMixer w/o data augmentation
python -m train.zinc metis.online False
# Running Graph MLPMixer w/o patch overlapping
python -m train.zinc metis.num_hops 0
# Running Graph MLPMixer w/ k-hop extension (replace k with 1,2,...)
python -m train.zinc metis.num_hops k
# Running Graph MLPMixer using random partition
python -m train.zinc metis.enable False
# Running Graph MLPMixer w/ different numbers of Patches P
python -m train.zinc metis.n_patches P
```
# Run Graph ViT
We provide five different versions of Graph ViT. The versions primarily differ in the graph-based multi-head attention (gMHA) function used.
```
# Hadamard Attention
python -m train.zinc model.gMHA_type Hadamard pos_enc.patch_num_diff 0
# Standard/Full Attention
python -m train.zinc model.gMHA_type Standard pos_enc.patch_num_diff -1
# Graph Attention
python -m train.zinc model.gMHA_type Graph pos_enc.patch_num_diff 0
# Kernel Attention
python -m train.zinc model.gMHA_type Kernel pos_enc.patch_num_diff 8
# Additive Attention
python -m train.zinc model.gMHA_type Additive pos_enc.patch_num_diff 0
```
# Run MP-GNNs
Run MP-GNNs by setting n_patches to zero.
```
python -m train.zinc model.name MPGNN metis.n_patches 0
python -m train.peptides_func model.name MPGNN metis.n_patches 0
...
```
# Reproducibility
Use `run.sh` to run the codes and reproduce the published results.