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Getting Started

This page provides basic tutorials about the usage of SimVP. For installation instructions, please see Install.

Training and Testing with a Single GPU

You can perform single/multiple GPU training and testing with tools/train.py and tools/test.py. We provide descriptions of some essential arguments.

python tools/train.py \
    --dataname ${DATASET_NAME} \
    --method ${METHOD_NAME} \
    --config_file ${CONFIG_FILE} \
    --ex_name ${EXP_NAME} \
    --auto_resume \
    --batch_size ${BATCH_SIZE} \
    --lr ${LEARNING_RATE} \

Description of arguments:

  • --dataname (-d) : The name of dataset, default to be mmnist.
  • --method (-m) : The name of the video prediction method to train or test, default to be SimVP.
  • --config_file (-c) : The path of a model config file, which will provide detailed settings for a video prediction method.
  • --ex_name : The name of the experiment under the res_dir. Default to be Debug.
  • --auto_resume : Whether to automatically resume training when the experiment was interrupted.
  • --batch_size (-b) : Training batch size, default to 16.
  • --lr : The basic training learning rate, defaults to 0.001.

An example of single GPU training with SimVP+gSTA on Moving MNIST dataset.

bash tools/prepare_data/download_mmnist.sh
python tools/train.py -d mmnist --lr 1e-3 -c ./configs/mmnist/simvp/SimVP_gSTA.py --ex_name mmnist_simvp_gsta

An example of single GPU testing with SimVP+gSTA on Moving MNIST dataset.

python tools/test.py -d mmnist -c configs/mmnist/simvp/SimVP_gSTA.py --ex_name mmnist_simvp_gsta