MLP-Mixer

MLP-Mixer: An all-MLP Architecture for Vision, arxiv

PaddlePaddle training/validation code and pretrained models for MLP-Mixer.

The official TF implementation is here.

This implementation is developed by PaddleViT.

MLP-Mixer Model Overview

Update

• Update (2021-08-11): Model FLOPs and # params are uploaded.
• Update (2021-08-11): Code is released and ported weights are uploaded.

Models Zoo

Model	Acc@1	Acc@5	#Params	FLOPs	Image Size	Crop_pct	Interpolation	Link
mlp_mixer_b16_224	76.60	92.23	60.0M	12.7G	224	0.875	bicubic	google/baidu(xh8x)
mlp_mixer_l16_224	72.06	87.67	208.2M	44.9G	224	0.875	bicubic	google/baidu(8q7r)

*The results are evaluated on ImageNet2012 validation set.

Note: MLP-Mixer weights are ported from timm )

Notebooks

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

*(coming soon)*

Requirements

• Python>=3.6
• yaml>=0.2.5
• PaddlePaddle>=2.1.0
• yacs>=0.1.8

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 ./mixer_b16_224.pdparams, to use the mixer_b16_224 model in python:

from config import get_config
from mlp_mixer import build_mlp_mixer as build_model
# config files in ./configs/
config = get_config('./configs/mixer_b16_224.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./mixer_b16_224.pdparams')
model.set_dict(model_state_dict)

Evaluation

To evaluate MLP-Mixer 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/mixer_b16_224.yaml \
    -dataset=imagenet2012 \
    -batch_size=16 \
    -data_path=/path/to/dataset/imagenet/val \
    -eval \
    -pretrained=/path/to/pretrained/model/mixer_b16_224  # .pdparams is NOT needed

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/mixer_b16_224.yaml \
    -dataset=imagenet2012 \
    -batch_size=16 \
    -data_path=/path/to/dataset/imagenet/val \
    -eval \
    -pretrained=/path/to/pretrained/model/mixer_b16_224  # .pdparams is NOT needed

Training

To train the MLP-Mixer Transformer model on ImageNet2012 with single GPU, run the following script using command line:

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
  -cfg=./configs/mixer_b16_224.yaml \
  -dataset=imagenet2012 \
  -batch_size=32 \
  -data_path=/path/to/dataset/imagenet/train

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/mixer_b16_224.yaml \
    -dataset=imagenet2012 \
    -batch_size=16 \
    -data_path=/path/to/dataset/imagenet/train

Visualization Attention Map

(coming soon)

Reference

@article{tolstikhin2021mlp,
  title={Mlp-mixer: An all-mlp architecture for vision},
  author={Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner, Thomas and Yung, Jessica and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and others},
  journal={arXiv preprint arXiv:2105.01601},
  year={2021}
}