README.md

FSWP - fast wave-front prediction

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Install

1. Project structure

First of all, make sure the project structure matches the one below:

├── fswp_train.yml
├── fswp_run.yml
├── _data
│   └── # pickle files for model training
├── checkpoints
│   └── # .pth models 
├── models
│   ├── unet #unet trainer
│   └── convlstm #convlstm trainer
├── utils
└── docker 

2. Download the data

Download convlstm training data, and data unet training. Put the received datasets in the data folder.

3. Download models

Download the unet model and put it in a folder checkpoints/unet

Download the convlstm model and put it in a folder checkpoints/convlstm

4. Download requirements.txt

pip install -r requirements.txt

Docker

We use crafting to automate our experiments. You can find an example of running such a pipeline in run.yaml file. You need to have installed Docker, Nvidia drivers, and crafting package.

The crafting package is available in PyPI:

pip install crafting

To build the image run the command below in docker folder:

sh build.sh

To run an experiment specify target command in command field in run.yaml file and call crafting:

crafting configs/docker_run.yaml

Start train

train.sh -fswp_train.yml

RUN

Test on data

python3 utils/predict_on_tdataset.py ./configs/run_solar_fswp.yaml

Test on simulator

python3 utils/run_on_env.py configs/run_star_fswp.yaml False checkpoints/convlstm/CONV_LSTM_run_EricWright_94ed9438-8fae-4fc8-8afa-85409d0c6f46.pth