Awesome

Data Level LTH for ViTs

Introduction

Code for paper "Data Level Lottery Ticket Hypothesis for Vision Transformers".

This paper is accepted by IJCAI 2023.

Preparation

DeiT

torch==1.9.0
torchvision==0.10.0
timm==0.4.12
tensorboardX==2.4
torchprofile==0.0.4
lmdb==1.2.1
pyarrow==5.0.0

Swin

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

opencv-python==4.4.0.46 
termcolor==1.1.0 
yacs==0.1.8

Training

Replace the data_path in shell file with the ImageNet dataset path

Download the pretrained model from official github of DeiT or Swin

DeiT

Normal Training Sequence:

bash run-deit-small-keeprate0.xx-load-pretrain-fineture.sh
bash run-deit-small-keeprate0.xx-LTH.sh
bash run-deit-small-keeprate0.xx-RR.sh

You can revise the keep rate as you want:

1. revise the shell file name: "-keeprate0.xx-"
2. revise the save_path in shell file: "exp-deit-small-keeprate0.xx"
3. revise the --lottery at "-keeprate0.xx-"
4. revise the --base_keep_rate 0.xx
5. revise the output txt file name at the last row: "-keeprate0.xx-"
(6.) revise the epoch when training the "RR": --epochs xxx

DeiT as teacher to train Swin

bash run-swin-tiny-keeprate0.xxx-LTH.sh

LV-ViT

Generate token label data for training of LV-ViT

python3 generate_label.py \
        /path/to/imagenet/train \
        /path/to/save/label_top5_train_nfnet \
        --model dm_nfnet_f6 \
        --pretrained \
        --img-size 576 \
        -b 32 \
        --crop-pct 1.0