Uses Lightning CLI and supports multiple models. Configuration files are located in configs/.
In order to run for classification:
python main.py fit --model FireCastNet --config configs/config.yaml
Other models can be run using:
python main.py fit --model GRU --config configs/gru-config.yaml
python main.py fit --model ConvGRU --config configs/conv-gru-config.yaml
python main.py fit --model ConvLSTM --config configs/conv-lstm-config.yaml
python main.py fit --model UTAE --config configs/utae-config.yaml
Adjust the configuration file and name it with a regr suffix.
python main.py fit --model FireCastNet --config configs/config-regr.yaml
You need to install pytorch, DGL and lightning.
DGL version 2.0.0 from
curl -LO https://data.dgl.ai/wheels/cu121/dgl-2.0.0%2Bcu121-cp310-cp310-manylinux1_x86_64.whl
Download the SeasFire dataset from zenodo. Note it is 44GB.
Unzip the dataset to a folder of your choice. Reference the dataset from the config file.
This work is part of the SeasFire project, which deals with ”Earth System Deep Learning for Seasonal Fire Forecasting” and is funded by the European Space Agency (ESA) in the context of the ESA Future EO-1 Science for Society Call.