Network Compression via Central Filter

Related tags

Deep Learning Central-Filter
Overview

Network Compression via Central Filter

Environments

The code has been tested in the following environments:

  • Python 3.8
  • PyTorch 1.8.1
  • cuda 10.2
  • torchsummary, torchvision, thop

Both windows and linux are available.

Pre-trained Models

CIFAR-10:

Vgg-16 | ResNet56 | DenseNet-40 | GoogLeNet

ImageNet:

ResNet50

Running Code

The experiment is divided into two steps. We have provided the calculated data and can skip the first step.

Similarity Matrix Generation

@echo off
@rem for windows
start cmd /c ^
"cd /D [code dir]  ^
& [python.exe dir]\python.exe rank.py ^
--arch [model arch name] ^
--resume [pre-trained model dir] ^
--num_workers [worker numbers] ^
--image_num [batch numbers] ^
--batch_size [batch size] ^
--dataset [CIFAR10 or ImageNet] ^
--data_dir [data dir] ^
--calc_dis_mtx True ^
& pause"
# for linux
python rank.py \
--arch [model arch name] \
--resume [pre-trained model dir] \
--num_workers [worker numbers] \
--image_num [batch numbers] \
--batch_size [batch size] \
--dataset [CIFAR10 or ImageNet] \
--data_dir [data dir] \
--calc_dis_mtx True

Model Training

The experimental results and related configurations covered in this paper are as follows.

1. VGGNet

Architecture Compress Rate Params Flops Accuracy
VGG-16(Baseline) 14.98M(0.0%) 313.73M(0.0%) 93.96%
VGG-16 [0.3]+[0.2]*4+[0.3]*2+[0.4]+[0.85]*4 2.45M(83.6%) 124.10M(60.4%) 93.67%
VGG-16 [0.3]*5+[0.5]*3+[0.8]*4 2.18M(85.4%) 91.54M(70.8%) 93.06%
VGG-16 [0.3]*2+[0.45]*3+[0.6]*3+[0.85]*4 1.51M(89.9%) 65.92M(79.0%) 92.49% 
python main_win.py \
--arch vgg_16_bn \
--resume [pre-trained model dir] \
--compress_rate [0.3]*2+[0.45]*3+[0.6]*3+[0.85]*4 \
--num_workers [worker numbers] \
--epochs 30 \
--lr 0.001 \
--lr_decay_step 5 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10

2. ResNet-56

Architecture Compress Rate Params Flops Accuracy
ResNet-56(Baseline) 0.85M(0.0%) 125.49M(0.0%) 93.26%
ResNet-56 [0.]+[0.2,0.]*9+[0.3,0.]*9+[0.4,0.]*9 0.53M(37.6%) 86.11M(31.4%) 93.64%
ResNet-56 [0.]+[0.3,0.]*9+[0.4,0.]*9+[0.5,0.]*9 0.45M(47.1%) 75.7M(39.7%) 93.59%
ResNet-56 [0.]+[0.2,0.]*2+[0.6,0.]*7+[0.7,0.]*9+[0.8,0.]*9 0.19M(77.6%) 40.0M(68.1%) 92.19% 
python main_win.py \
--arch resnet_56 \
--resume [pre-trained model dir] \
--compress_rate [0.]+[0.2,0.]*2+[0.6,0.]*7+[0.7,0.]*9+[0.8,0.]*9 \
--num_workers [worker numbers] \
--epochs 30 \
--lr 0.001 \
--lr_decay_step 5 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10

3.DenseNet-40

Architecture Compress Rate Params Flops Accuracy
DenseNet-40(Baseline) 1.04M(0.0%) 282.00M(0.0%) 94.81%
DenseNet-40 [0.]+[0.3]*12+[0.1]+[0.3]*12+[0.1]+[0.3]*8+[0.]*4 0.67M(35.6%) 165.38M(41.4%) 94.33%
DenseNet-40 [0.]+[0.5]*12+[0.3]+[0.4]*12+[0.3]+[0.4]*9+[0.]*3 0.46M(55.8%) 109.40M(61.3%) 93.71% 
# for linux
python main_win.py \
--arch densenet_40 \
--resume [pre-trained model dir] \
--compress_rate [0.]+[0.5]*12+[0.3]+[0.4]*12+[0.3]+[0.4]*9+[0.]*3 \
--num_workers [worker numbers] \
--epochs 30 \
--lr 0.001 \
--lr_decay_step 5 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10

4. GoogLeNet

Architecture Compress Rate Params Flops Accuracy
GoogLeNet(Baseline) 6.15M(0.0%) 1520M(0.0%) 95.05%
GoogLeNet [0.2]+[0.7]*15+[0.8]*9+[0.,0.4,0.] 2.73M(55.6%) 0.56B(63.2%) 94.70%
GoogLeNet [0.2]+[0.9]*24+[0.,0.4,0.] 2.17M(64.7%) 0.37B(75.7%) 94.13% 
python main_win.py \
--arch googlenet \
--resume [pre-trained model dir] \
--compress_rate [0.2]+[0.9]*24+[0.,0.4,0.] \
--num_workers [worker numbers] \
--epochs 1 \
--lr 0.001 \
--save_id 1 \
--weight_decay 0. \
--data_dir [dataset dir] \
--dataset CIFAR10

python main_win.py \
--arch googlenet \
--from_scratch True \
--resume finally_pruned_model/googlenet_1.pt \
--num_workers 2 \
--epochs 30 \
--lr 0.01 \
--lr_decay_step 5,15 \
--save_id 1 \
--weight_decay 0.005 \
--data_dir [dataset dir] \
--dataset CIFAR10

4. ResNet-50

Architecture Compress Rate Params Flops Top-1 Accuracy Top-5 Accuracy
ResNet-50(baseline) 25.55M(0.0%) 4.11B(0.0%) 76.15% 92.87%
ResNet-50 [0.]+[0.1,0.1,0.2]*1+[0.5,0.5,0.2]*2+[0.1,0.1,0.2]*1+[0.5,0.5,0.2]*3+[0.1,0.1,0.2]*1+[0.5,0.5,0.2]*5+[0.1,0.1,0.1]+[0.2,0.2,0.1]*2 16.08M(36.9%) 2.13B(47.9%) 75.08% 92.30%
ResNet-50 [0.]+[0.1,0.1,0.4]*1+[0.7,0.7,0.4]*2+[0.2,0.2,0.4]*1+[0.7,0.7,0.4]*3+[0.2,0.2,0.3]*1+[0.7,0.7,0.3]*5+[0.1,0.1,0.1]+[0.2,0.3,0.1]*2 13.73M(46.2%) 1.50B(63.5%) 73.43% 91.57%
ResNet-50 [0.]+[0.2,0.2,0.65]*1+[0.75,0.75,0.65]*2+[0.15,0.15,0.65]*1+[0.75,0.75,0.65]*3+[0.15,0.15,0.65]*1+[0.75,0.75,0.65]*5+[0.15,0.15,0.35]+[0.5,0.5,0.35]*2 8.10M(68.2%) 0.98B(76.2%) 70.26% 89.82% 
python main_win.py \
--arch resnet_50 \
--resume [pre-trained model dir] \
--data_dir [dataset dir] \
--dataset ImageNet \
--compress_rate [0.]+[0.1,0.1,0.4]*1+[0.7,0.7,0.4]*2+[0.2,0.2,0.4]*1+[0.7,0.7,0.4]*3+[0.2,0.2,0.3]*1+[0.7,0.7,0.3]*5+[0.1,0.1,0.1]+[0.2,0.3,0.1]*2 \
--num_workers [worker numbers] \
--batch_size 64 \
--epochs 2 \
--lr_decay_step 1 \
--lr 0.001 \
--save_id 1 \
--weight_decay 0. \
--input_size 224 \
--start_cov 0

python main_win.py \
--arch resnet_50 \
--from_scratch True \
--resume finally_pruned_model/resnet_50_1.pt \
--num_workers 8 \
--epochs 40 \
--lr 0.001 \
--lr_decay_step 5,20 \
--save_id 2 \
--batch_size 64 \
--weight_decay 0.0005 \
--input_size 224 \
--data_dir [dataset dir] \
--dataset ImageNet
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Comments
  • 在rank.py中出现了bug，_index的定义没有找到。

    在rank.py中出现了bug，_index的定义没有找到。 

    for i,feature in enumerate(conv_outs): 
	print('--->',i,feature)
	if args.arch == 'densenet_40':
		conv_out = LayerActivations(feature,_index[i][0],_index[i][1])
	else :
		conv_out = LayerActivations(feature)
	model.eval()

    在上述代码中的_index并没有设置定义，请问该如何修改？

    opened by linklist2 1
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