Revisiting Dynamic Convolution via Matrix Decomposition

A pytorch implementation of DCD. If you use this code in your research please consider citing

@article{li2021revisiting, title={Revisiting Dynamic Convolution via Matrix Decomposition}, author={Li, Yunsheng and Chen, Yinpeng and Dai, Xiyang and Liu, Mengchen and Chen, Dongdong and Yu, Ye and Yuan, Lu and Liu, Zicheng and Chen, Mei and Vasconcelos, Nuno}, journal={arXiv preprint arXiv:2103.08756}, year={2021} }

Requirements

  • Hardware: PC with NVIDIA Titan GPU.
  • Software: Ubuntu 16.04, CUDA 10.0, Anaconda3, pytorch 1.0.0
  • Python package
    • conda install --quiet --yes pytorch==1.0.0 torchvision==0.2.1 cuda100 -c pytorch
    • pip install tensorboard tensorboardX pillow==6.1

Evaluate DCD on ImageNet

The pre-trained model can be downloaded here ResNet-50 and MobileNetV2x1.0

DCD for ResNet-50

python main.py -a resnet50_dcd -d /path/to/imagenet/ -b 256 -c /path/to/output -j 48 --input-size 224 --dropout 0.1 --weight /path/to/resnet50_dcd.pth.tar --evaluate

DCD for MobileNetV2x1.0

python main.py -a mobilenetv2_dcd -d /path/to/imagenet/ -b 512 -c /path/to/output --width-mult 1.0 -j 48 --input-size 224 --dropout 0.1 --fc-squeeze 16 --weight mv2x1.0_dcd.pth.tar --evaluate

Train DCD on ImageNet

DCD for ResNet-50

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py -a resnet50_dcd -d /path/to/imagenet/ -b 256 --epochs 120 --lr-decay schedule --lr 0.1 --wd 1e-4 -c /path/to/output -j 48 --input-size 224 --label-smoothing 0.1 --dropout 0.1 --mixup 0.2

DCD for MobileNetV2x1.0

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py -a mobilenetv2_dcd -d /path/to/imagenet/ --epochs 300 --lr-decay cos --lr 0.1 --wd 2e-5 -c /path/to/output --width-mult 1.0 -j 48 --input-size 224 --label-smoothing 0.1 --dropout 0.2 -b 512 --mixup 0.2 --fc-squeeze 16

GitHub

https://github.com/liyunsheng13/dcd