This code is for the paper: "Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processing", which is submitted to ICASSP2021.

We propose a branchy structure for GNN-based point cloud classification to speedup edge inference. We adopt branch structures for early exiting the main branch to reduce the on-device computational cost and introduce joint source-channel coding (JSCC) to reduce the communication overhead.

In the experiment, we have four exit points.

Note that the main branch in the framework is based on DGCNN.

Pytorch
h5py
sklearn

ModelNet40

1. Pretrain a DGCNN model based on the code or download it from here. (./pretrained/model.1024.t7)
2. Train the branch network by python edge_main.py --num_p=1024 --use_sgd=True --model EXIT1 --channel_noise 0.1.

Note that --model could be EXIT1, EXIT2, EXIT3, and EXIT4.

--channel_noise is the standard deviation in the AWGN channel. The output of the encoder is normalized based on the l-2 norm, and the average signal power is 1. So, channel_noise = 0.1 means SNR = 20dB.

python edge_main.py --num_points=1024 --k=20 --eval=True --model_path= (saved model path) --channel_noise 0.056

(2023-May-29) I have corrected some errors and updated Fig. 2 and Fig. 3 in the arxiv version.