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FF_Only

README.md

Do You Even Need Attention? A Stack of Feed-Forward Layers Does

Surprisingly Well on ImageNet, arxiv

PaddlePaddle training/validation code and pretrained models for FF_Only.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

drawing

FF_Only Model Overview

Update

Update (2021-09-14): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 Image Size Crop_pct Interpolation Link
ff_tiny 61.28 84.06 224 0.875 bicubic google/baidu(mjgd)
ff_base 74.82 91.71 224 0.875 bicubic google/baidu(m1jc)

*The results are evaluated on ImageNet2012 validation set.

Note: FF_Only weights are ported from here.

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 ./linear_base.pdparams, to use the linear_base model in python:

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

Evaluation

To evaluate FF_Only 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/ff_base.yaml' \
    -dataset='imagenet2012' \
    -batch_size=8 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./linear_base'
Run evaluation using multi-GPUs: 
sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=4,5,6,7 \
python main_multi_gpu.py \
    -cfg='./configs/ff_base.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./linear_base'

Training

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

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
    -cfg='./configs/ff_base.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/ff_base.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \

Visualization Attention Map

(coming soon)

Reference

@article{melaskyriazi2021doyoueven,
  title={Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet},
  author={Luke Melas-Kyriazi},
  journal=arxiv,
  year=2021
}