DyRep: Bootstrapping Training with Dynamic Re-parameterization

Official implementation for paper "DyRep: Bootstrapping Training with Dynamic Re-parameterization", CVPR 2022.

By Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu.

Updates

March 11, 2022

The code is available at image_classification_sota.

Getting started

Clone training code

git clone https://github.com/hunto/image_classification_sota

The prepare your environment and datasets following the README.md in image_classification_sota.

Implementation of DyRep

The core concept of DyRep is in lib/models/utils/dyrep.py.

Reproducing our results

CIFAR

CIFAR-10

sh tools/dist_train.sh 1 configs/strategies/DyRep/cifar.yaml nas_model --model-config configs/models/VGG/vgg16_cifar10.yaml --dyrep --experiment dyrep_cifar10_vgg16

CIFAR-100

sh tools/dist_train.sh 1 configs/strategies/DyRep/cifar.yaml nas_model --model-config configs/models/VGG/vgg16_cifar100.yaml --dyrep --dataset cifar100 --experiment dyrep_cifar100_vgg16

ImageNet

ResNets

sh tools/dist_train.sh 8 configs/strategies/DyRep/resnet.yaml resnet50 --dyrep --experiment dyrep_imagenet_res50

MobileNetV1

sh tools/dist_train.sh 8 configs/strategies/DyRep/mbv1.yaml mobilenet_v1 --dyrep --experiment dyrep_imagenet_mbv1

RepVGG

DyRep-A2

sh tools/dist_train.sh 8 configs/strategies/DyRep/repvgg_baseline.yaml timm_repvgg_a2 --dyrep --dyrep_recal_bn_every_epoch --experiment dyrep_imagenet_repvgg_a2

DyRep-B2g4 and DyRep-B3

sh tools/dist_train.sh 8 configs/strategies/DyRep/repvgg_strong.yaml timm_repvgg_b2g4 --dyrep --dyrep_recal_bn_every_epoch --experiment dyrep_imagenet_repvgg_b2g4

Deploying the Trained DyRep Models to Inference Models

sh tools/dist_convert.sh 8 ${CONFIG} ${MODEL} --resume ${CHECKPOINT}

For example, if you want to deploy the trained ResNet-50 model with the best checkpoint, run

sh tools/dist_convert.sh 8 configs/strategies/DyRep/resnet.yaml resnet50 --dyrep --resume experiments/dyrep_imagenet_res50/best.pth.tar

Then it will run test before and after deployment to ensure the accuracy will not drop.

The final weights of the inference model will be saved in experiments/dyrep_imagenet_res50/convert/model.ckpt.

Results

Citation

@article{huang2022dyrep,
  title={DyRep: Bootstrapping Training with Dynamic Re-parameterization},
  author={Huang, Tao and You, Shan and Zhang, Bohan and Du, Yuxuan and Wang, Fei and Qian, Chen and Xu, Chang},
  journal={arXiv preprint arXiv:2203.12868},
  year={2022}
}

scott-mao/DyRep