Pytorch implementation of Deep Compression

Dataset: CIFAR10 Models: VGG16, Resnet18~

• Image classification
• Image segmentation
• Image detection

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.

Resnet is still underfitting.

To do pruning..

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.

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

To train your own model

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

Training script

You can train multiple pruned resnet by script.

sh prune.sh

TODO:

Quantization HuffmanCoding