Pytorch implementation of Deep Compression

Dataset: CIFAR10 Models: VGG16, Resnet18~

To start with..

clone the repo to your local.

git clone https://github.com/kentaroy47/Deep-Compression.Pytorch

The CIFAR10 trained models are in checkpoint.

VGG	Res18
85.8%	89.8%

Resnet is still underfitting.

The pretrained resnet18 should be in checkpoint/res18.t7. The val accuracy is about 90%.

# This will prune the default resnet18 with 50% parameters of conv2d.
# It will do 20 epochs of retraining as well.
python prune.py

Pruning results for resnet18.

no pruning	10% pruned	25% pruned	50% pruned	75% pruned
89.8%	90.3%	91.2%	91.7%	91.9%

Even with 50% pruned weights, the accuracy is about the same (90%).

You can try arbitary pruning rates:

# This will prune 75% of conv parameters.
python prune.py --prune 0.75

I provided a CIFAR 10 trained resnet18 checkpoint to get started with.

If you want to train your own network, do:


# for vgg and resnet50
python train_cifar10.py --net vgg
python train_cifar10.py --net res50

You can train multiple pruned resnet by script.

sh prune.sh

Quantization HuffmanCoding