dmlc--dgl
Hongzhi (Steve), Chen
704bcaf6dd
Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
Point Transformer
This model is implemented on August 27, 2021 when there is no official code released.
Thus we implemented this model based on the code from https://github.com/qq456cvb/Point-Transformers.
This is a reproduction of the paper: Point Transformer.
Performance
| Task | Dataset | Metric | Score - Paper | Score - DGL (Adam) | Score - DGL (SGD) | Time(s) - DGL |
|---|---|---|---|---|---|---|
| Classification | ModelNet40 | Accuracy | 93.7 | 92.0 | 91.5 | 117.0 |
| Part Segmentation | ShapeNet | mIoU | 86.6 | 84.3 | 85.1 | 260.0 |
- Time(s) are the average training time per epoch, measured on EC2 p3.8xlarge instance w/ Tesla V100 GPU.
How to Run
For point cloud classification, run with
python train_cls.py --opt [sgd/adam]
For point cloud part-segmentation, run with
python train_partseg.py --opt [sgd/adam]