SiMaN: Sign-to-Magnitude Network Binarization (Paper Link) .

Pytorch implementation of SiMaN (IEEE TPAMI 2022).

Its Conference of RBNN (NeurIPS 2020) is here (Paper, code)

Dependencies

• python 3.7
• pytorch 1.1.0
• torchvision 0.3.0
• numpy 1.17.2

Tips

We suggest reading our RBNN in NeurIPS'20 first (Paper, Code).

Any problem, please contact the first author (Email: lmbxmu@stu.xmu.edu.cn) or the third author (Email: ianhsu@stu.xmu.edu.cn).

Training on CIFAR-10

python -u main.py \
--gpus 0 \
--seed 123 \
--model resnet18_1w1a (or resnet20_1w1a or vgg_small_1w1a) \
--results_dir ./result \
--data_path [DATA_PATH] \
--dataset cifar10 \
--epochs 400 \
--lr 0.1 \
-b 256 \
-bt 128 \
--lr_type cos \
--warm_up \
--weight_decay 5e-4 \

Results on CIFAR-10.

Quantized model Link	batch_size	batch_size_test	epochs	Top-1
resnet18_1w1a	256	128	400	92.5
resnet20_1w1a	256	128	400	87.4
vgg_small_1w1a	256	128	400	92.5

To ensure the reproducibility, please refer to our training details provided in the links for our quantized models.

To verify the performance of our quantized models on CIFAR-10, please use the following command:

python -u main.py \
--gpus 0 \
-e [best_model_path] \
--model resnet20_1w1a (resnet18_1w1a or vgg_small_1w1a) \
--data_path [DATA_PATH] \
--dataset cifar10 \
-bt 128 \

Training on ImageNet

python -u main.py \
--gpus 0,1 \
--model resnet18_1w1a (or resnet34_1w1a) \
--results_dir ./result \
--data_path [DATA_PATH] \
--dataset imagenet \
--epochs 150 \
--lr 0.1 \
-b 512 \
-bt 256 \
--lr_type cos \
--use_dali \

We provide two types of dataloaders by nvidia-dali and Pytorch respectively. They use the same data augmentations, including random crop and horizontal flip. We empirically find that the dataloader by Pytorch can offer a better accuracy performance. They may have different code implementations. Anyway, we haven't figured it out yet. However, nvidia-dali shows its extreme efficiency in processing data which well accelerates the network training. The reported experimental results are on the basis of nvidia-dali. If interested, you can try dataloader by Pytorch via removing the optional argument --use_dali to obtain a better performance.

Nvidia-dali package

# for CUDA 10
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda100
# for CUDA 11
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda110

Results on ImageNet

Quantized model Link	batch_size	batch_size_test	epochs	use_dali	Top-1	Top-5
resnet18_1w1a	512	256	150	✔	60.1	82.3
resnet34_1w1a	512	256	150	✔	63.9	84.8

To ensure the reproducibility, please refer to our training details provided in the links for our quantized models.
Small tips for further boosting the performance of our method: (1) removing the optional argument --use_dali as discussed above; (2) increasing the training epochs; (3) enlarging the batch size for training.

To verify the performance of our quantized models on ImageNet, please use the following command:

python -u main.py \
--gpu 0 \
-e [best_model_path] \
--model resnet18_1w1a (or resnet34_1w1a)\
--dataset imagenet \
--data_path [DATA_PATH] \
-bt 256 \
--use dali