Filter Sketch for Network Pruning (Link).

Pruning neural network model via filter sketch.

Tips

Any problem, free to contact the authors via emails: lmbxmu@stu.xmu.edu.cn or shaojieli@stu.xmu.edu.cn. Do not post issues with github as much as possible, just in case that I could not receive the emails from github thus ignore the posted issues.

Citation

If you find FilterSketch useful in your research, please consider citing:

@article{lin2020filter,
  title={Filter Sketch for Network Pruning},
  author={Lin, Mingbao and Ji, Rongrong and Li, Shaojie and Ye, Qixiang and Tian, Yonghong and Liu, Jianzhuang and Tian, Qi},
  journal={arXiv preprint arXiv:2001.08514},
  year={2020}
}

Pre-trained Models

We provide the pre-trained models used in our paper.

CIFAR-10

| ResNet56 | ResNet110 |GoogLeNet |

ImageNet

| ResNet50 |

Result Models

We provide our pruned models in the experiments, along with their training loggers and configurations.

	DataSet	Sketch Rate	Flops (Prune Rate）	Params (Prune Rate）	Top-1 Accuracy	Top-5 Accuracy	Download
ResNet56	CIFAR-10	[0.6]*27	73.36M(41.5%)	0.50M(41.2%)	93.19%	-	Link
ResNet110	CIFAR-10	[0.9]*3+[0.4]*24+[0.3]*24+[0.9]*3	92.84M(63.3%)	0.69M(59.9%)	93.44%	-	Link
GoogLeNet	CIFAR-10	[0.25]*9	0.59B(61.1%)	2.61M(57.6%)	94.88%	-	Link
ResNet50	ImageNet	[0.2]*16	0.93B(77.3%)	7.18M(71.8%)	69.43%	89.23%	Link
ResNet50	ImageNet	[0.4]*16	1.51B(63.1%)	10.40M(59.2%)	73.04%	91.18%	Link
ResNet50	ImageNet	[0.6]*16	2.23B(45.5%)	14.53M(43.0%)	74.68%	92.17%	Link
ResNet50	ImageNet	[0.7]*16	2.64B(35.5%)	16.95M(33.5%)	75.22%	92.41%	Link

Performance of FilterSketch using ResNet-56 under different compression rates.

DataSet	Sketch Rate	Flops (Prune Rate）	Params (Prune Rate）	Top-1 Accuracy	Download
CIFAR-10	[0.1]*27	11.43M(91.0%)	0.08M(90.45%)	87.38%	Link
CIFAR-10	[0.2]*27	24.54M(80.6%)	0.16M(81.0%)	90.19%	Link
CIFAR-10	[0.3]*27	35.61M(71.9%)	0.25M(70.6%)	91.65%	Link
CIFAR-10	[0.4]*27	48.72M(61.5%)	0.33M(61.1%)	92.00%	Link
CIFAR-10	[0.5]*27	63.78M(49.6%)	0.43M(49.8%)	92.29%	Link
CIFAR-10	[0.6]*27	73.36M(41.5%)	0.50M(41.2%)	93.19%	Link
CIFAR-10	[0.7]*27	87.31M(31.0%)	0.59M(31.1%)	93.36%	Link
CIFAR-10	[0.8]*27	98.40M(22.3%)	0.68M(20.8%)	93.40%	Link
CIFAR-10	[0.9]*27	111.5M(11.9%)	0.75M(11.3%)	93.44%	Link
CIFAR-10	[0.9]*3+[0.1]*10+[0.1]*10+[0.6]*4	32.47M(74.4%)	0.24M(71.8%)	91.20%	Link
CIFAR-10	[0.7]*3+[0.4]*10+[0.4]*10+[0.9]*4	62.63M(50.5%)	0.48M(43.3%)	92.94%	Link
CIFAR-10	[0.8]*3+[0.5]*10+[0.8]*10+[0.9]*4	88.05M(30.4%)	0.68M(20.6%)	93.65%	Link

Running Code

The code has been tested using Pytorch1.3 and CUDA10.0 on Ubuntu16.04.

Filter Sketch

You can run the following code to sketch model on Cifar-10:

python sketch_cifar.py 
--data_set cifar10 
--data_path ../data/cifar10/
--sketch_model ./experiment/pretrain/resnet56.pt 
--job_dir ./experiment/resnet56/sketch/
--arch resnet 
--cfg resnet56 
--lr 0.01
--lr_decay_step 50 100
--num_epochs 150 
--gpus 0
--sketch_rate [0.6]*27
--weight_norm_method l2

You can run the following code to sketch model on Imagenet:

python sketch_imagenet.py 
--data_set imagenet 
--data_path ../data/imagenet/
--sketch_model ./experiment/pretrain/resnet50.pth 
--job_dir ./experiment/resnet50/sketch/
--arch resnet 
--cfg resnet50 
--lr 0.1
--lr_decay_step 30 60
--num_epochs 90 
--gpus 0
--sketch_rate [0.6]*16
--weight_norm_method l2

Test Our Performance

Follow the command below to verify our pruned models:

python test.py 
--data_set cifar10 
--data_path ../data/cifar10 
--arch resnet 
--cfg resnet56 
--sketch_model ./experiment/result/sketch_resnet56.pt 
--sketch_rate [0.6]*27 
--gpus 0

Get FLOPS and Params

You can use the following command to install the thop python package when you need to calculate the flops of the model:

pip install thop

python get_flops_params.py 
--data_set cifar10 
--input_image_size 32 
--arch resnet 
--cfg resnet56
--sketch_rate [0.6]*27

Remarks

The number of pruning rates required for different networks is as follows:

	CIFAR-10	ImageNet
ResNet56	27	-
ResNet110	54	-
GoogLeNet	9	-
ResNet50	-	16

Other Arguments

optional arguments:
  -h, --help            show this help message and exit
  --gpus GPUS [GPUS ...]
                        Select gpu_id to use. default:[0]
  --data_set DATA_SET   Select dataset to train. default:cifar10
  --data_path DATA_PATH
                        The dictionary where the input is stored.
                        default:/home/lishaojie/data/cifar10/
  --job_dir JOB_DIR     The directory where the summaries will be stored.
                        default:./experiments
  --arch ARCH           Architecture of model. default:resnet
  --cfg CFG             Detail architecuture of model. default:resnet56
  --num_epochs NUM_EPOCHS
                        The num of epochs to train. default:150
  --train_batch_size TRAIN_BATCH_SIZE
                        Batch size for training. default:128
  --eval_batch_size EVAL_BATCH_SIZE
                        Batch size for validation. default:100
  --momentum MOMENTUM   Momentum for MomentumOptimizer. default:0.9
  --lr LR               Learning rate for train. default:1e-2
  --lr_decay_step LR_DECAY_STEP [LR_DECAY_STEP ...]
                        the iterval of learn rate. default:50, 100
  --weight_decay WEIGHT_DECAY
                        The weight decay of loss. default:5e-4
  --start_conv START_CONV
                        The index of Conv to start sketch, index starts from
                        0. default:1
  --sketch_rate SKETCH_RATE
                        The proportion of each layer reserved after sketching
                        convolution layer sketch. default:None
  --sketch_model SKETCH_MODEL
                        Path to the model wait for sketch. default:None
  --weight_norm_method WEIGHT_NORM_METHOD
                        Select the weight norm method. default:None
                        Optional:l2