wnov / channel_pruning_lasso

channel pruning for accelerating very deep neural networks

Channel pruning for accelerating very deep neural networks

This repo contains the PyTorch implementation for paper channel pruning for accelerating very deep neural networks.

Prune

We just support vgg-series network pruning, you can type command as follow to execute pruning.

python3 main.py --model vgg16_bn \
                --batch_size 100 \
                --calib_batch 50 \
                --calib_dir /data/imagenet1k_calib \
                --valid_dir /data/ILSVRC2012/val \
                --pruner lasso \
                --ckpt ./vgg16_bn-6c64b313.pth

type	layer	sparsity	remain channal
conv1_2	5	0.5	32
conv2_1	9	0.5	64
conv2_2	12	0.5	64
conv3_1	16	0.5	130
conv3_2	19	0.5	128
conv3_3	22	0.5	205
conv4_1	26	0.2	409
conv4_2	29	0.2	415
conv4_3	32	0.2	512

After pruning:
  Top1 acc=59.728%
  Parameter: 135.452 M
  FLOPs: 7466.797M

Finetune

python3 train.py --data_root /data/ILSVRC2012 \
                 --model vgg16_bn_x \
                 --lr 1e-03 \
                 --lr_type cos \
                 --dataset imagenet \
                 --n_epoch 30 \
                 --batch_size 128 \
                 --ckpt_path vgg16_pruned.pth.tar \
                 --seed 1

Test

python3 test.py --model vgg16_bn_x \
                --checkpoint ./vgg16_pruned_finetune.pth.tar \
                --imagenet_path /data/ILSVRC2012/

After finetuning:
  Top1 acc=73.584%, Top5=91.490%

Orig:
  Top1 acc=73.476%, Top5=91.536%