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gyshgx868 / graph-ter

The official PyTorch implementation of the CVPR2020 paper "GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations".
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GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations

This repository is the official PyTorch implementation of the following paper:

Xiang Gao, Wei Hu, Guo-Jun Qi, "GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations," In Proceedings of the IEEE/CVF Conferences on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, June 2020.

Requirements

Note: A cuDNN error CUDNN_STATUS_NOT_SUPPORTED occurs when you use PyTorch with a version greater than 1.2.0, but we have not figured out this issue in this code.

Datasets and Pre-trained Models

To evaluate the model, ModelNet40 and ShapeNet Part dataset in HDF5 format are required to be downloaded and unzipped to the data folder.

Download ModelNet40 dataset for classification task by running the following commands:

cd ./data
wget https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip
unzip modelnet40_ply_hdf5_2048.zip
rm modelnet40_ply_hdf5_2048.zip

Download ShapeNet Part dataset for segmentation task by running the following commands:

cd ./data
wget https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip
unzip shapenet_part_seg_hdf5_data.zip
rm shapenet_part_seg_hdf5_data.zip
mv hdf5_data shapenet_part

The pre-trained models can be downloaded from OneDrive or GoogleDrive, and manually place the classification and segmentation folders into the pretrained folder.

Usage

We take classification task as an example to introduce how to use our code, and segmentation task is similar.

Testing Pre-trained Models

You can run the following command to reproduce the results in our paper:

python main_classification.py --phase test --device 0 1 2 3 --test-batch-size 32 --data-path ./data --transform [full class name] --backbone [backbone checkpoints] --classifier [classifier checkpoints]

You should specify the [full class name] (e.g., graph_ter_cls.transforms.GlobalRotate), the [backbone checkpoints], and the [classifier checkpoints]. For instance:

python main_classification.py --phase test --device 0 1 2 3 --test-batch-size 32 --data-path ./data --transform graph_ter_cls.transforms.GlobalRotate --backbone ./pretrained/classification/backbone_global_rotate_aniso.pt --classifier ./pretrained/classification/classifier_global_rotate_aniso.pt

You can also run the following command to make it easier to evaluate the pre-trained models:

python main_classfication.py --config ./config/classification/global_rotate_aniso.yaml

Note that we test our model on 4 NVIDIA RTX 2080Ti GPUs. You can use --device to specify the indices of GPUs and use --test-batch-size to fit the memory, or appoint --use-cuda false to use CPUs for evaluation.

Training

To train a feature extractor in an unsupervised fashion, run

python main_classification.py --phase backbone --device 0 1 2 3 --train-batch-size 32 --data-path ./data --transform [full class name]

Again, you should specify the [full class name] (e.g., graph_ter_cls.transforms.GlobalRotate).

After training the feature extractor, you need to train the classifier by running the following command:

python main_classification.py --phase classifier --device 0 1 2 3 --train-batch-size 32 --data-path ./data --transform [full class name] --backbone [backbone checkpoints]

The [backbone checkpoints] refers to checkpoints of the trained feature extractor.

The log files, network parameters, and TensorBoard logs will be saved to results folder by default. We can use TensorBoard to view the training progress:

tensorboard --logdir ./results/tensorboard

For more hyper-parameters, please refer to graph_ter_cls/tools/configuration.py and graph_ter_seg/tools/configuration.py.

Reference

Please cite our paper if you use any part of the code from this repository:

@inproceedings{gao2020graphter,
  title={Graph{TER}: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations},
  author={Gao, Xiang and Hu, Wei and Qi, Guo-Jun},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month={June},
  year={2020}
}

Acknowledgement

Our code is released under MIT License (see LICENSE for details). Some of the code in this repository was borrowed from the following repositories: