This is the codebase our paper published in [Buildsys 2020].
We have used the trainer.py script to train the model, you can use it in the following way
python trainer.py unpruned_modelpython trainer.py normal_pruningpython trainer.py iterative_pruningpython trainer.py tensor_decompositionpython trainer.py fully_shared_mtlpython trainer.py fully_shared_mtl_pruningpython trainer.py fully_shared_mtl_iterative_pruning
Please note that, in order to execute the normal_pruning,iterative_pruning and tensor_decomposition, you first need to train the unpruned_model
Please note that, in order to execute the fully_shared_mtl_pruning and fully_shared_mtl_iterative_pruning, you first need to train the fully_shared_mtl
You can find the models we used to train here (Link will be posted to the google drive by November 30, 2020).
Once you finish training the model or after downloading the pre-trained models, you can do the following:
By executing python test.py we get the results of all the models for each fold for all the appliances.
You can execute python flops_compute.py to create the CSV file which contains the info about the number of MFLOPs needed for a single pass.
You can execute python time_compute.py to create the CSV file which contains the info about the inference time and model size.