The training script will train the model using the train and validation datasets.
The saved model and the tensorboard logs will be save in summary/{test-name}.
CUDA_VISIBLE_DEVICES=0 python train.py --test-name 'test_name' --n-shots={n_shots} --initialization={init}
--trajectory-learning={trajectory_learning} --sub-lr={sub_lr} --lr={rec_lr}
Where:
test-name: Name of the experiment.n-shots: Number of shots.initialization: Initialization of the trajectory, one of 'cartesian'/'radial'.trajectory-learning: If set to True will learn both the reconstruction network and the trajectory, else will learn only the reconstruction.sub-lr: learning rate of the subsampling layer.lr: Learning rate of the reconstruction network.
Full list of possible arguments can be seen in train.py.
You need first to install all the dependencies using:
pip install -r req.txt
I guess that we can upgrade to newer versions of must of this libraries.