Mobile-Former: Bridging MobileNet and Transformer, arxiv

PaddlePaddle training/validation code for MobileFormer.

This implementation is developed by PaddleViT.

MobileFormer Model Overview

MobileFormer Sub-Block Overview

Update

• Update(2022-04-11): Code is updated.
• Update(2021-11-26): Code is released.

Models Zoo

Model	Acc@1	Acc@5	#Params	FLOPs	Image Size	Crop_pct	Interpolation	Link
mobileformer_26m	*	*	3.227M	26M	224	0.875	bicubic	*
mobileformer_52m	*	*	3.513M	52M	224	0.875	bicubic	*
mobileformer_96m	*	*	4.595M	96M	224	0.875	bicubic	*
mobileformer_151m	*	*	7.616M	151M	224	0.875	bicubic	*
mobileformer_214m	*	*	9.416M	214M	224	0.875	bicubic	*
mobileformer_294m	*	*	11.392M	294M	224	0.875	bicubic	*
mobileformer_508m	*	*	14.013M	508M	224	0.875	bicubic	*

*The results are evaluated on ImageNet2012 validation set.

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 ./mobileformer_26m.pdparams, to use the mobileformer_26m model in python:

from config import get_config
from mobileformer import build_mobileformer as build_model
# config files in ./configs/
config = get_config('./configs/mobileformer_26m.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./mobileformer_26m.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/mobileformer_26m.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./mobileformer_26m.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/mobileformer_26m.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.

Arguments

• -cfg: path of model config file (.yaml), stored in ./configs.
• -dataset: dataset name, e.g., imagenet2012, cifar10, cifar100.
• -data_path: dataset folder path
• -batch_size: batch size，default: 32.
• -image_size: input image size，default224.
• -num_classes: number of classes, default: 1000.
• -output: output folder for storing weights and logs，default: ./output.
• -pretrained: pretrain model weights file path, (.pdparams file ext is NOT needed) default: None.
• -resume: resume model weight and opt file path, (.paparams and .pdopts file ext are NOT needed, default: None.
• -last_epoch: start epoch，default: None.
• -save_freq: number of epochs to save checkpoint，default: 1.
• -log_freq: number of iters to print logging，default: 100.
• -validate_freq: number of epochs to do validation during training，default: 10.
• -accum_iter: number of iteration for iter accumulation, default: 1.
• -num_workers: number of workers for data loading，default: 1.
• -ngpus: number of GPUs to use，you can control GPUs by CUDA_VISIBLE_DEVICES, just set this to -1 default: -1.
• -eval: start eval mode.
• -amp: start amp training.

-cfg,-dataset and -data_path in main_single_gpu.py and main_multi_gpu.py are MUST-HAVE settings.

Reference

@misc{chen2021mobileformer,
      title={Mobile-Former: Bridging MobileNet and Transformer}, 
      author={Yinpeng Chen and Xiyang Dai and Dongdong Chen and Mengchen Liu and Xiaoyi Dong and Lu Yuan and Zicheng Liu},
      year={2021},
      eprint={2108.05895},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}