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ConvMLP

README.md

ConvMLP: Hierarchical Convolutional MLPs for Vision, arxiv

PaddlePaddle training/validation code and pretrained models for ConvMLP.

The official and 3rd party pytorch implementation are here.

This implementation is developed by PPViT.

drawing

ViP Model Overview

Update

Update (2021-09-26): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
convmlp_s 76.76 93.40 9.0M 2.4G 224 0.875 bicubic google/baidu(3jz3)
convmlp_m 79.03 94.53 17.4M 4.0G 224 0.875 bicubic google/baidu(vyp1)
convmlp_l 80.15 95.00 42.7M 10.0G 224 0.875 bicubic google/baidu(ne5x)

*The results are evaluated on ImageNet2012 validation set.

Note: ConvMLP 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 ./convmlp_s.pdparams, to use the convmlp_s model in python:

from config import get_config
from convmlp import build_convmlp as build_model
# config files in ./configs/
config = get_config('./configs/convmlp_s.yaml')
# build model
model = build_model(config)
# load pretrained weights, .pdparams is NOT needed
model_state_dict = paddle.load('./convmlp_s7')
model.set_dict(model_state_dict)

Evaluation

To evaluate ConvMLP 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/convmlp_s.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./convmlp_s'
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/convmlp_s.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./convmlp_s'

Training

To train the ConvMLP Transformer 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/convmlp_s.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/convmlp_s.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \

Visualization Attention Map

(coming soon)

Reference

@article{li2021convmlp,
      title={ConvMLP: Hierarchical Convolutional MLPs for Vision}, 
      author={Jiachen Li and Ali Hassani and Steven Walton and Humphrey Shi},
      year={2021},
      eprint={2109.04454},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}