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Towards Compact CNNs via Collaborative Compression

PyTorch implementation for Towards Compact CNNs via Collaborative Compression.

Running Code

In this code, you can run ResNet/DenseNet/VGGNet/GoogLeNet model on CIFAR10/ImageNet2012 dataset. The code has been tested by Python 3.6.8, Pytorch 1.7.1 and CUDA 10.0.

Running Example

Train

CIFAR-10

ResNet-56 (compression ratio = 50%)

python compress.py  --dataset cifar10 \
                    --net resnet56 \
                    --pretrained True \
                    --checkpoint pth/resnet56.pth \
                    --train_dir tmp/resnet56_CC_0.5 \
                    --train_batch_size 128 \
                    --com_ratio 0.5

or

sh compress.sh

DenseNet-40 (compression ratio = 50%)

python compress.py  --dataset cifar10 \
                    --net densenet40 \
                    --pretrained True \
                    --checkpoint pth/densenet40.pth \
                    --train_dir tmp/densenet40_CC_0.5 \
                    --train_batch_size 128 \
                    --com_ratio 0.5

ImageNet-2012

Setting ImageNet-2012 directory in dataset/imagenet.py

ResNet-50 (compression ratio = 50%)

python compress.py  --dataset imagenet \
                    --net resnet50 \
                    --pretrained True \
                    --checkpoint pth/resnet50.pth \ # download from torchvision
                    --train_dir tmp/resnet50_CC_0.5 \
                    --train_batch_size 256 \
                    --com_ratio 0.5