Graviti-AI / prediction-challenge-predictor

a reference Implementation for the prediction challenge predictor

Predictor

prediction-challenge predictor application

Description

The client will use on_env to store the historical trajectory (10 frames) sent by the simulator, and use fetch_my_state to get the predicted results (30 frames).

In the ./predictor/*,

• predictor.py is an abstract class, you need to implement all the abstract methods in your own predictor.
• echo_predictor.py is a simple echo predictor, namely returns back the last frame in the historical trajectory.
• lstm_predictor.py is a pytorch-based predictor which is trained on the interaction dataset and has 0.3 MoN performance on the MA scenario. lstm.pt stores the model parameters.

Prerequisites

• python 3.7

• docker

• grpc && protobuf

  • please follow the instruction in the simulator/readme.md

  • pip install grpcio
    pip install protobuf

• pytorch or tensorflow to support your predictor.

Build and Run

Prepare

use protoc to generate python version protocols for communication. each time the simulator.proto updated, you need generate them again.

protoc -I ../proto/ --grpc_out=. --plugin=protoc-gen-grpc=`which grpc_python_plugin` ../proto/simulator.proto
protoc -I ../proto/ --python_out=. ../proto/simulator.proto

If this fails, you can try

python -m grpc_tools.protoc --proto_path=../proto/ --python_out=. --grpc_python_out=. ../proto/simulator.proto

You may get a warning when using grpc_tools.protoc, but it should still execute succesfully and generate the simulator_pb2.py and simulator_pb2_grpc files (provided that you have grpc_tools installed).

Run locally

# simulator should be launched before predictor, where
# -s sepcify the simulator service address
# -p specify the simulator service port
python3 main.py -s 127.0.0.1 -p 50051

Docker

• You need to put the dependencies (pytorch, numpy, ....) in the requirement.text, then

cd deploy
./make_image.sh my_predictor