MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization paper

Dependence

• See requirements.txt

Usage

2-, 4-, 6-, 8-bit settings

Usage of CIFAR100

python main_cifar.py --data PathForCIFAR \
 --visible_gpus '0' --multiprocessing_distributed False \
 --dist_url 'tcp://127.0.0.1:23456' \
 --workers 4  --datasetsname cifar100 \
 --arch 'resnet20_quant' \
 --batch_size 128  \
 --epochs 200 --lr_m 0.1 --weight_decay 0.0001 \
 --log_dir PathForSave --bit_list 2468 --gpu 0 

Usage of ImageNet

For resnet:

python main_resnet.py --data PathForImageNet \
--visible_gpus '0,1,2,3' --multiprocessing_distributed True \
--dist_url 'tcp://127.0.0.1:23456' --workers 16  \
--arch 'resnet18_quant' --batch_size 256  \
--epochs 90 --lr_m 0.1 --weight_decay 0.0001 \
--log_dir PathForSave --bit_list 2468

or for Mv1:

python main_mv1.py --data PathForImageNet \
--visible_gpus '0,1,2,3' --multiprocessing_distributed True \
--dist_url 'tcp://127.0.0.1:23456' --workers 16  \
--arch 'mv1_quant' --batch_size 256  \
--epochs 90 --lr_m 0.1 --weight_decay 0.0001 \
--log_dir PathForSave --bit_list 2468

2-, 3-, and 4-bit settings

1, Open the main_resnet.py

2, Comment lines 25 and 26. Uncomment 27, 28

3, run:

python main_resnet.py --data PathForImageNet \
--visible_gpus '0,1,2,3' --multiprocessing_distributed True \
--dist_url 'tcp://127.0.0.1:23456' --workers 16  \
--arch 'resnet18_quant' --batch_size 256  \
--epochs 90 --lr_m 0.1 --weight_decay 0.0001 \
--log_dir PathForSave --bit_list 234

Trained models

here

Acknowledgments

Code is implemented based on PalQuant. We are very grateful for their excellent work.