Edge-Conditioned Convolutions on Graphs
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ECC - Edge-Conditioned Convolution on Graphs

This is the official PyTorch port of the original Torch implementation of our CVPR'17 paper Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs https://arxiv.org/abs/1704.02901 for the task of graph classification.

Code structure

  • ./ecc/* - General-purpose graph convolution and pooling modules (graphs represented in igraph framework).
  • ./pointcloud_* - Point cloud dataset loading and conversion of point clouds to graphs.
  • ./* - Model definition and the main script


  1. Install PyTorch and then torchnet with pip install git+https://github.com/pytorch/tnt.git@master.

  2. The point cloud classification part of the code relies on Open3D. In my experience installation from source is more reliable than pip-installing.

  3. Install additional Python packages: pip install future python-igraph tqdm transforms3d pynvrtc cupy.

The code was tested on Ubuntu 14.04 with Python 2.7 or 3.6.

Point cloud classification

Sydney Urban Objects

The dataset can be downloaded from [http://www.acfr.usyd.edu.au/papers/data/sydney-urban-objects-dataset.tar.gz] and extracted into ./datasets (or SYDNEY_PATH has to be changed in pointcloud_dataset.py otherwise). To train the model described in the paper, run

for FOLD in 0 1 2 3; do \
CUDA_VISIBLE_DEVICES=0 python main.py --lr 0.1 --lr_steps '[200,245]' --epochs 250  --batch_size 32 \
--model_config 'i_0.1_0.2, c_16,b,r, c_32,b,r, m_0.25_0.5, c_32,b,r, c_32,b,r, m_0.75_1.5, c_64,b,r, m_1.5_1.5,a_1e10_1e10, f_64,b,r,d_0.2,f_14' \
--fnet_widths '[16,32]' --fnet_llbias 0 --pc_augm_scale 1.2 --pc_augm_mirror_prob 0.2 --pc_augm_input_dropout 0.1 \
--nworkers 3 --edgecompaction 0 --cvfold $FOLD --odir "results/sydney_${FOLD}"; \

The average of the final F1 scores (found in respective JSON log files trainlog.txt in the last f1_test field) should be around 0.78, subject to GPU non-determinism.


ModelNet is a dataset of meshes, which for our purpose had to be converted to synthetic point clouds by uniformly sampling 1000 points on mesh faces according to face area (code was based on PCL example, not in this repository). We provide the resulting point clouds for download here. The archive can be extracted to ./datasets (or MODELNET10_PATH and MODELNET40_PATH have to be changed in pointcloud_dataset.py otherwise).

To train the ModelNet10 model described in the paper, run:

CUDA_VISIBLE_DEVICES=0 python main.py \
--dataset modelnet10 --test_nth_epoch 25 --lr 0.1 --lr_steps '[50,100,150]' --epochs 175 --batch_size 64 --batch_parts 4 \
--model_config 'i_1_2, c_16,b,r, c_32,b,r, m_2.5_7.5, c_32,b,r, c_32,b,r, m_7.5_22.5, c_64,b,r, m_1e10_1e10, f_64,b,r,d_0.2,f_10' \
--fnet_llbias 0 --fnet_widths '[16,32]' --pc_augm_scale 1.2 --pc_augm_mirror_prob 0.2 --pc_augm_input_dropout 0.1 \
--nworkers 3 --edgecompaction 1 --odir results/modelnet10

To train the ModelNet40 model described in the paper, run:

CUDA_VISIBLE_DEVICES=0 python main.py \
--dataset modelnet40 --test_nth_epoch 25 --lr 0.1 --lr_steps '[30,60,90]' --epochs 100 --batch_size 64 --batch_parts 4 \
--model_config 'i_1_2, c_24,b,r, c_48,b,r, m_2.5_7.5, c_48,b,r, c_48,b,r, m_7.5_22.5, c_96,b,r, m_1e10_1e10, f_64,b,r,d_0.2,f_40'  \
--fnet_llbias 0 --fnet_widths '[16,32]' --pc_augm_scale 1.2 --pc_augm_mirror_prob 0.2 --pc_augm_input_dropout 0.1 \
--nworkers 3 --edgecompaction 1 --edge_mem_limit 1000 --odir results/modelnet40  

Test set mean class accuracies should be around 89 and 82 respectively ('acc_test' is mean instance accuracy, 'cacc_test' is mean class accuracy in trainlog.txt), subject to GPU non-determinism.

In order to evaluate the trained model with test-time voting, run:

CUDA_VISIBLE_DEVICES=2 python main.py \
--dataset modelnet10 --epochs -1 --test_sample_avging vote --nworkers 3 --edgecompaction 1 \
--resume results/modelnet10/model.pth.tar --odir results/modelnet10_vote

Custom datasets

Adding a custom dataset should be relatively easy by adaptating functions get_modelnet_info() and get_modelnet() in pointcloud_dataset.py and registering them in main.py. The model has to be configured properly in the argument --model_config to use sensible grid and neighborhood sizes for graph pyramid construction (layers i, m, and a, see models.py).

General graph classification



  • The model may be quite memory hungry for point cloud training especially, where nearly all edges have their unique edge features and thus also filter weights. This can be currently circumvented in three ways:
    • Compacting edges with identical attributes by setting --edgecompaction 1
    • Computing convolutions and poolings in shards of #E edges by --edge_mem_limit #E
    • Evaluating the batch in multiple runs by setting --batch_parts to a divisor of --batch_size. A yet unimplemented alternative would be to implement (randomized) clustering in cloud_edge_feats().


    author = {Martin Simonovsky and Nikos Komodakis},
    title = {Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs},
    url = {https://arxiv.org/abs/1704.02901},
    booktitle = {CVPR},
    year = {2017}}