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Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

Official PyTorch implementation

Requirements

We provide installation instructions for ImageNet classification experiments here, based on ConvNeXt instructions.

Dependency Setup

Create an new conda virtual environment

conda create -n convnext python=3.8 -y
conda activate convnext

Install Pytorch>=1.8.0, torchvision>=0.9.0 following official instructions. For example:

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install timm==0.3.2 tensorboardX six

The results in the paper are produced with torch==1.8.0+cu111 torchvision==0.9.0+cu111 timm==0.3.2.

Dataset Preparation

Download the ImageNet-1K classification dataset and structure the data as follows:

/path/to/imagenet-1k/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class2/
      img4.jpeg

Train models

Original ConvNeXt-Tiny

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1440 main.py \
--model convnext_tiny \
--drop_path 0.1 \
--batch_size 32  --update_freq 16 \
--lr 4e-3 \
--model_ema true --model_ema_eval true \
--data_set IMNET \
--data_path </path/to/imagenet> \
--output_dir </path/to/output/dir> \
 --epochs 300 --warmup_epochs 20 \

ConvNeXt-Tiny Baseline (circular convolutions)

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1440 main.py \
--model convnext_afc_tiny \
--blurpool_kwargs "{\"filt_size\": 1, \"scale_l2\":false}" \
--activation gelu \
--normalization_type C \
--drop_path 0.1 \
--batch_size 32  --update_freq 16 \
--lr 4e-3 \
--model_ema true --model_ema_eval true \
--data_set IMNET \
--data_path </path/to/imagenet> \
--output_dir </path/to/output/dir> \
 --epochs 300 --warmup_epochs 20 \

ConvNeXt-Tiny-AFC

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1440 main.py \
--model convnext_afc_tiny \
--drop_path 0.1 \
--blurpool_kwargs "{\"filter_type\": \"ideal\", \"scale_l2\":false}" \
--activation up_poly_per_channel \
--activation_kwargs "{\"in_scale\":7, \"out_scale\":7, \"train_scale\":true}" \
--model_kwargs "{\"stem_mode\":\"activation_residual\", \"stem_activation\": \"lpf_poly_per_channel\"}" \
--stem_activation_kwargs "{\"in_scale\":7, \"out_scale\":7, \"train_scale\":true, \"cutoff\":0.75}" \
--normalization_type CHW2 \
--batch_size 32  --update_freq 16 \
--lr 4e-3 \
--model_ema true --model_ema_eval true \
--data_set IMNET \
--data_path </path/to/imagenet> \
--output_dir </path/to/output/dir> \
 --epochs 300 --warmup_epochs 20 \

ConvNeXt-Tiny-APS

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1440 main.py \
--model convnext_aps_tiny \
--drop_path 0.1 \
 --blurpool_kwargs "{\"filt_size\": 1}" \
--activation gelu \
--normalization_type C \
--batch_size 32  --update_freq 16 \
--lr 4e-3 \
--model_ema true --model_ema_eval true \
--data_set IMNET \
--data_path </path/to/imagenet> \
--output_dir </path/to/output/dir> \
 --epochs 300 --warmup_epochs 20 \

Checkpoints

Trained models can be Found in: https://huggingface.co/hmichaeli/convnext-afc

Acknowledgement

This repository is built using Truly shift invariant CNNs and ConvNeXt repositories.

Citation

If you find this repository helpful, please consider citing:

@InProceedings{Michaeli_2023_CVPR,
    author    = {Michaeli, Hagay and Michaeli, Tomer and Soudry, Daniel},
    title     = {Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {16333-16342}
}