A Unified Framework for Soft Threshold Pruning

This directory contains the code reproducing this paper. The code is modified based on the open-source code of STR.

Dependency

The major dependencies of this code are list as below. The detailed ones are listed in requirements.txt

# Name                    Version
cudatoolkit               10.2.89
cudnn                     8.2.1.32
numpy                     1.21.4
python                    3.7.11 
pytorch                   1.10.0
tensorboard               2.7.0
torchvision               0.11.1
pyyaml                    6.0

Environment

The running of code requires NVIDIA GPU and has been tested on CUDA 10.2 and Ubuntu 16.04. The hardware platform used in our experiments is shown below.

• GPU: Tesla V100
• CPU: Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz

Each trial requires 8 GPUs.

Usage

Note: You may need to specify different names for each experiment using --name, or it would be grueling to find the result of an exact trial. The setting of final threshold is in Appendix I of the paper.

Dense training on ResNet-50:

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/resnet50-dense.yaml --print-freq 4096 --data <PATH to ImageNet>

Dense training on MobileNet-V1:

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/mobilenetv1-dense.yaml --print-freq 4096 --data <PATH to ImageNet>

S-LATS on ResNet-50:

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/resnet50-prune.yaml --gradual sinp --flat-width <final threshold D> --print-freq 4096 --data <PATH to ImageNet> --name <Name of this experiment>

S-LATS on ResNet-50 (1024 batch size):

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/resnet50-prune.yaml --gradual sinp --flat-width <final threshold D> --batch-size 1024 --lr 0.512 --print-freq 4096 --data <PATH to ImageNet> --name <Name of this experiment>

PGH on ResNet-50:

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/resnet50-prune.yaml --gradual sinppgh --flat-width <final threshold D> --print-freq 4096 --data <PATH to ImageNet> --name <Name of this experiment>

LATS on ResNet-50:

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/resnet50-prune.yaml --gradual sinp --flat-width <final threshold D> --print-freq 4096 --data <PATH to ImageNet> --low-freq --name <Name of this experiment>

S-LATS on MobileNet-V1:

python main.py --multigpu 0,1,2,3,4,5,6,7 --config configs/reparam/mobilenetv1-prune.yaml --gradual sinp --flat-width <final threshold D> --print-freq 4096 --data <PATH to ImageNet> --name <Name of this experiment>