Ashchenlovelive / Pruning_filters_for_efficient_convnets

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

Pruning Filters For Efficient ConvNets

Unofficial PyTorch implementation of pruning VGG on CIFAR-10 Data set

Reference: Pruning Filters For Efficient ConvNets, ICLR2017

Contact: Minseong Kim ([e-mail])

Requirements

• torch (version: 1.2.0)
• torchvision (version: 0.4.0)
• Pillow (version: 6.1.0)
• matplotlib (version: 3.1.1)
• numpy (version: 1.16.5)

Usage

Arguments

• --train-flag: Train VGG on CIFAR Data set
• --save-path: Path to save results, ex) trained_models/
• --load-path: Path to load checkpoint, add 'checkpoint.pht' with save_path, ex) trained_models/checkpoint.pth
• --resume-flag: Resume the training from checkpoint loaded with load-path
• --prune-flag: Prune VGG
• --prune-layers: List of target convolution layers for pruning, ex) conv1 conv2
• --prune-channels: List of number of channels for pruning the prune-layers, ex) 4 14
• --independent-prune-flag: Prune multiple layers by independent strategy
• --retrain-flag: Retrain the pruned nework
• --retrain-epoch: Number of epoch for retraining pruned network
• --retrain-lr: Number of epoch for retraining pruned network

Example Scripts

Train VGG on CIFAR-10 Data set

python main.py --train-flag --data-set CIFAR10 --vgg vgg16_bn --save-path ./trained_models/

Prune VGG by 'greedy strategy'

python main.py --prune-flag --load-path ./trained_models/check_point.pth --save-path ./trained_models/pruning_reuslts/ --prune-layers conv1 conv2 --prune-channels 1 1 

Prune VGG by 'independent strategy'

python main.py --prune-flag --load-path ./trained_models/check_point.pth --save-path ./trained_models/pruning_reuslts/ --prune-layers conv1 conv2 --prune-channels 1 1 --independent-prune-flag

Retrain the pruned network

python main.py --prune-flag --load-path ./trained_models/check_point.pth --save-path ./trained_models/pruning_reuslts/ --prune-layers conv1 --prune-channels 1 --retrain-flag --retrain-epoch 20 --retrain-lr 0.001

Results

Absolute sum of filter weights for each layer of VGG-16 trained on CIFARA-10

• This graph was created in jupyter notebook. You can make the graph yourself.

Pruning filters with the lowest absolute weights sum and their corresponding test accuracies on CIFAR-10

• This graph was created in jupyter notebook. You can make the graph yourself.

Prune and retrain for each single layer of VGG-16 on CIFAR-10

• This graph was created in jupyter notebook. You can make the graph yourself.