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HaloNet

README.md

Scaling Local Self-Attention for Parameter Efficient Visual Backbones, arxiv

PaddlePaddle training/validation code and pretrained models for HaloNet.

The official pytorch implementation is N/A.

This implementation is developed by PaddleViT.

drawing

HaloNet local self-attention architecture

Update

  • Update (2022-04-11): Code is updated.
  • Update (2021-12-09): Initial code and ported weights are released.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
halonet26t 79.10 94.31 12.5M 3.2G 256 0.95 bicubic google/baidu
halonet50ts 81.65 95.61 22.8M 5.1G 256 0.94 bicubic google/baidu

*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 ./halonet_26t_256.pdparams, to use the halonet_26t_256 model in python:

from config import get_config
from halonet import build_halonet as build_model
# config files in ./configs/
config = get_config('./configs/halonet_26t_256.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./halonet_26t_256.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/halonet_26t_256.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./halonet_26t_256.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/halonet_26t_256.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

@inproceedings{vaswani2021scaling,
  title={Scaling local self-attention for parameter efficient visual backbones},
  author={Vaswani, Ashish and Ramachandran, Prajit and Srinivas, Aravind and Parmar, Niki and Hechtman, Blake and Shlens, Jonathon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12894--12904},
  year={2021}
}