Official implementation of FORCE algorithm from Progressive Skeletonization: Trimming more fat from a network at initialization (https://arxiv.org/abs/2006.09081)
You can create a conda environment with all the necessary libraries with the command:
conda create --name myenv --file conda-env.txt
You will need to download the Tiny-Imagenet and Imagenet datasets. For Tiny-Imagenet modify the path specified in experiments/datasets.py For Imagenet you will need to specify it when runing the code.
To run an experiment with CIFAR10/100 or Tiny Imagenet datasets, run:
python train_cifar_tiny_imagenet.py --network_name vgg19 --pruning_factor 0.01 --prune_method 1 --dataset CIFAR10 --num_steps 60 --mode exp --num_batches 1
Alternatively, you can change the --dataset option with CIFAR100 or tiny_imagenet. Bear in mind that for CIFAR100 you will need to set --num_batches to 10 and for tiny_imagenet to 20. You may also change the architecture and use resnet50 for instance.
To run an experiment with Imagenet run:
python train_imagenet.py /path_to_dataset/ --network-name resnet50 --pruning_factor 0.05 --prune_method 1 --num_steps 60 --mode exp --num_batches 40 --epochs 90
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