RCAR2021_MultiAgentTrajPrediction

code and materials for Multi-Agent Trajectory Predition Based on Graph Neural Network

Note:This repo stops to be updated, and for further work of us, you can switch to this repo which keeps on updating.

Heterogeneous GNN

main.py: main function of LOG Analysis model training process
parameters.py: parameters management of the training process
dataFormat.py: basic data format of player, ball and game data
referee.py: referee command defined in proto file
dataPreprocess.py: preprocess the text file we get from our vision module, converting into formatted data we can use and doing Min-Max normalization
SSLDataset.py: construct the graph structure for future gnn training
mys2v.py: basic graph neural network we use
pna/mypna.py: Heterogeneous PNAConv
heterogeneous/myheter.py: Heterogeneous graph neural network frame supporting different GNNs
Net.py: neural network we construct
debug/debug.py: draw gradient of graph neural network
visualize.py: draw pictures of our training result
testOurModel.py: load torch model and see results and also visualiztion