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PiT

README.md

Rethinking Spatial Dimensions of Vision Transformers, arxiv

PaddlePaddle training/validation code and pretrained models for PiT.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

drawing

PiT Model Overview

Update

  • Update (2021-12-08): Code is updated and ported weights are uploaded.
  • Update (2021-11-13): Code is released.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
pit_ti 72.91 91.40 4.8M 0.5G 224 0.9 bicubic google/baidu(ydmi)
pit_ti_distill 74.54 92.10 5.1M 0.5G 224 0.9 bicubic google/baidu(7k4s)
pit_xs 78.18 94.16 10.5M 1.1G 224 0.9 bicubic google/baidu(gytu)
pit_xs_distill 79.31 94.36 10.9M 1.1G 224 0.9 bicubic google/baidu(ie7s)
pit_s 81.08 95.33 23.4M 2.4G 224 0.9 bicubic google/baidu(kt1n)
pit_s_distill 81.99 95.79 24.0M 2.5G 224 0.9 bicubic google/baidu(hhyc)
pit_b 82.44 95.71 73.5M 10.6G 224 0.9 bicubic google/baidu(uh2v)
pit_b_distill 84.14 96.86 74.5M 10.7G 224 0.9 bicubic google/baidu(3e6g)

*The results are evaluated on ImageNet2012 validation set.

Teacher Model Link
RegNet_Y_160 google/baidu(gjsm)

Notebooks

We provide a few notebooks in aistudio to help you get started:

*(coming soon)*

Requirements

Data

ImageNet2012 dataset is used in the following folder structure:

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Usage

To use the model with pretrained weights, download the .pdparam weight file and change related file paths in the following python scripts. The model config files are located in ./configs/.

For example, assume the downloaded weight file is stored in ./swin_base_patch4_window7_224.pdparams, to use the swin_base_patch4_window7_224 model in python:

from config import get_config
from pit import build_pit as build_model
# config files in ./configs/
config = get_config('./configs/pit_ti.yaml')
# build model
model = build_model(config)
# load pretrained weights, .pdparams is NOT needed
model_state_dict = paddle.load('./pit_ti')
model.set_dict(model_state_dict)

Evaluation

To evaluate PiT model performance on ImageNet2012 with a single GPU, run the following script using command line:

sh run_eval.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
    -cfg='./configs/pit_ti.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./pit_ti'
Run evaluation using multi-GPUs: 
sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg='./configs/pit_ti.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./pit_ti'

Training

To train the PiT model on ImageNet2012 with single GPU, run the following script using command line:

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_singel_gpu.py \
  -cfg='./configs/pit_ti.yaml' \
  -dataset='imagenet2012' \
  -batch_size=32 \
  -data_path='/dataset/imagenet' \
Run training using multi-GPUs: 
sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg='./configs/pit_ti.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \

Visualization Attention Map

(coming soon)

Reference

@inproceedings{heo2021pit,
    title={Rethinking Spatial Dimensions of Vision Transformers},
    author={Byeongho Heo and Sangdoo Yun and Dongyoon Han and Sanghyuk Chun and Junsuk Choe and Seong Joon Oh},
    booktitle = {International Conference on Computer Vision (ICCV)},
    year={2021},
}