This repo is

(1) a PyTorch library that provides classical knowledge distillation algorithms on mainstream CV benchmarks,

(2) the official implementation of the CVPR-2022 paper: Decoupled Knowledge Distillation.

Decoupled Knowledge Distillation

Framework & Performance

Main Benchmark Results

On CIFAR-100:

Teacher Student	ResNet56 ResNet20	ResNet110 ResNet32	ResNet32x4 ResNet8x4	WRN-40-2 WRN-16-2	WRN-40-2 WRN-40-1	VGG13 VGG8
KD	70.66	73.08	73.33	74.92	73.54	72.98
DKD	71.97	74.11	76.32	76.23	74.81	74.68

Teacher Student	ResNet32x4 ShuffleNet-V1	WRN-40-2 ShuffleNet-V1	VGG13 MobileNet-V2	ResNet50 MobileNet-V2	ResNet32x4 MobileNet-V2
KD	74.07	74.83	67.37	67.35	74.45
DKD	76.45	76.70	69.71	70.35	77.07

On ImageNet:

Teacher Student	ResNet34 ResNet18	ResNet50 MobileNet-V1
KD	71.03	70.50
DKD	71.70	72.05

MDistiller

Introduction

MDistiller supports the following distillation methods on CIFAR-100, ImageNet and MS-COCO:

Method	Paper Link	CIFAR-100	ImageNet	MS-COCO
KD	https://arxiv.org/abs/1503.02531	✓	✓
FitNet	https://arxiv.org/abs/1412.6550	✓
AT	https://arxiv.org/abs/1612.03928	✓	✓
NST	https://arxiv.org/abs/1707.01219	✓
PKT	https://arxiv.org/abs/1803.10837	✓
KDSVD	https://arxiv.org/abs/1807.06819	✓
OFD	https://arxiv.org/abs/1904.01866	✓	✓
RKD	https://arxiv.org/abs/1904.05068	✓
VID	https://arxiv.org/abs/1904.05835	✓
SP	https://arxiv.org/abs/1907.09682	✓
CRD	https://arxiv.org/abs/1910.10699	✓	✓
ReviewKD	https://arxiv.org/abs/2104.09044	✓	✓	✓
DKD	https://arxiv.org/abs/2203.08679	✓	✓	✓

Installation

Environments:

Python 3.6
PyTorch 1.9.0
torchvision 0.10.0

Install the package:

sudo pip3 install -r requirements.txt
sudo python3 setup.py develop

Getting started

Wandb as the logger

The registeration: https://wandb.ai/home.
If you don't want wandb as your logger, set CFG.LOG.WANDB as False at mdistiller/engine/cfg.py.

Evaluation

You can evaluate the performance of our models or models trained by yourself.
Our models are at https://github.com/megvii-research/mdistiller/releases/tag/checkpoints, please download the checkpoints to ./download_ckpts

If test the models on ImageNet, please download the dataset at https://image-net.org/ and put them to ./data/imagenet

# evaluate teachers
python3 tools/eval.py -m resnet32x4 # resnet32x4 on cifar100
python3 tools/eval.py -m ResNet34 -d imagenet # ResNet34 on imagenet

# evaluate students
python3 tools/eval.p -m resnet8x4 -c download_ckpts/dkd_resnet8x4 # dkd-resnet8x4 on cifar100
python3 tools/eval.p -m MobileNetV1 -c download_ckpts/imgnet_dkd_mv1 -d imagenet # dkd-mv1 on imagenet
python3 tools/eval.p -m model_name -c output/your_exp/student_best # your checkpoints

Training on CIFAR-100

Download the cifar_teachers.tar at https://github.com/megvii-research/mdistiller/releases/tag/checkpoints and untar it to ./download_ckpts via tar xvf cifar_teachers.tar.

# for instance, our DKD method.
python3 tools/train.py --cfg configs/cifar100/dkd/res32x4_res8x4.yaml

# you can also change settings at command line
python3 tools/train.py --cfg configs/cifar100/dkd/res32x4_res8x4.yaml SOLVER.BATCH_SIZE 128 SOLVER.LR 0.1

Training on ImageNet

Download the dataset at https://image-net.org/ and put them to ./data/imagenet

# for instance, our DKD method.
python3 tools/train.py --cfg configs/imagenet/r34_r18/dkd.yaml

Training on MS-COCO

see detection.md

Extension: Visualizations

Jupyter notebooks: tsne and correlation_matrices

Custom Distillation Method

create a python file at mdistiller/distillers/ and define the distiller

from ._base import Distiller

class MyDistiller(Distiller):
    def __init__(self, student, teacher, cfg):
        super(MyDistiller, self).__init__(student, teacher)
        self.hyper1 = cfg.MyDistiller.hyper1
        ...

    def forward_train(self, image, target, **kwargs):
        # return the output logits and a Dict of losses
        ...
    # rewrite the get_learnable_parameters function if there are more nn modules for distillation.
    # rewrite the get_extra_parameters if you want to obtain the extra cost.
  ...

regist the distiller in distiller_dict at mdistiller/distillers/__init__.py
regist the corresponding hyper-parameters at mdistiller/engines/cfg.py
create a new config file and test it.

Citation

If this repo is helpful for your research, please consider citing the paper:

@article{zhao2022dkd,
  title={Decoupled Knowledge Distillation},
  author={Zhao, Borui and Cui, Quan and Song, Renjie and Qiu, Yiyu and Liang, Jiajun},
  journal={arXiv preprint arXiv:2203.08679},
  year={2022}
}

License

MDistiller is released under the MIT license. See LICENSE for details.

Acknowledgement

Thanks for CRD and ReviewKD. We build this library based on the CRD's codebase and the ReviewKD's codebase.
Thanks Yiyu Qiu and Yi Shi for the code contribution during their internship in MEGVII Technology.
Thanks Xin Jin for the discussion about DKD.

liaorongfan/mdistiller