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RepMLP

README.md

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition, arxiv

PaddlePaddle training/validation code and pretrained models for RepMLP.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

drawing

RepMLP Model Overview

Update

  • Update (2022-04-01): New code is refactored and weights are uploaded.
  • Update (2021-09-27): Model FLOPs and # params are uploaded.
  • Update (2021-09-14): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
repmlp_b_224 80.25 95.16 6.8M 68.3G 224 0.875 bilinear google/baidu
repmlp_b_256 81.10 95.50 9.8M 96.5G 256 0.875 bilinear google/baidu

*The results are evaluated on ImageNet2012 validation set.

Note: RepMLP weights are ported from here.

Data Preparation

ImageNet2012 dataset is used in the following file structure:

│imagenet/
├──train_list.txt
├──val_list.txt
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......
  • train_list.txt: list of relative paths and labels of training images. You can download it from: google/baidu
  • val_list.txt: list of relative paths and labels of validation images. You can download it from: google/baidu

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 weight file is downloaded in ./repmlp_b_224.pdparams, to use the repmlp_b_224 model in python:

from config import get_config
from repmlp import build_repmlp as build_model
# config files in ./configs/
config = get_config('./configs/repmlp_b_224.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./repmlp_b_224.pdparams')
model.set_state_dict(model_state_dict)

Evaluation

To evaluate model performance on ImageNet2012, run the following script using command line:

sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/repmlp_b_224.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./repmlp_b_224.pdparams' \
-amp

Note: if you have only 1 GPU, change device number to CUDA_VISIBLE_DEVICES=0 would run the evaluation on single GPU.

Training

To train the model on ImageNet2012, run the following script using command line:

sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/repmlp_b_224.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-amp

Note: it is highly recommanded to run the training using multiple GPUs / multi-node GPUs.

## Reference

@article{ding2021repmlp, title={RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition}, author={Ding, Xiaohan and Xia, Chunlong and Zhang, Xiangyu and Chu, Xiaojie and Han, Jungong and Ding, Guiguang}, journal={arXiv preprint arXiv:2105.01883}, year={2021} }@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 }