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Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks [CVPR 2023]

This is the official Pytorch/PytorchLightning implementation of the paper:

Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
Jierun Chen, Shiu-hong Kao, Hao He, Weipeng Zhuo, Song Wen, Chul-Ho Lee, S.-H. Gary Chan
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

We propose a simple yet fast and effective partial convolution (PConv), as well as a latency-efficient family of architectures called FasterNet.

Image Classification

1. Dependency Setup

Create an new conda virtual environment

conda create -n fasternet python=3.9.12 -y
conda activate fasternet

Clone this repo and install required packages:

git clone https://github.com/JierunChen/FasterNet
cd FasterNet/
pip install -r requirements.txt

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

3. Pre-trained Models

name resolution acc #params FLOPs model
FasterNet-T0 224x224 71.9 3.9M 0.34G model
FasterNet-T1 224x224 76.2 7.6M 0.85G model
FasterNet-T2 224x224 78.9 15.0M 1.90G model
FasterNet-S 224x224 81.3 31.1M 4.55G model
FasterNet-M 224x224 83.0 53.5M 8.72G model
FasterNet-L 224x224 83.5 93.4M 15.49G model

4. Evaluation

We give an example evaluation command for a ImageNet-1K pre-trained FasterNet-T0 on a single GPU:

python train_test.py -c cfg/fasternet_t0.yaml \
--checkpoint_path model_ckpt/fasternet_t0-epoch=281-val_acc1=71.9180.pth \
--data_dir ../../data/imagenet --test_phase -g 1 -e 125
  • For evaluating other model variants, change -c, --checkpoint_path accordingly. You can get the pre-trained models from the tables above.
  • For multi-GPU evaluation, change -g to a larger number or a list, e.g., 8 or 0,1,2,3,4,5,6,7. Note that the batch size for evaluation should be changed accordingly, e.g., change -e from 125 to 1000.

To measure the latency on CPU/ARM and throughput on GPU (if any), run

python train_test.py -c cfg/fasternet_t0.yaml \
--checkpoint_path model_ckpt/fasternet_t0-epoch=281-val_acc1=71.9180.pth \
--data_dir ../../data/imagenet --test_phase -g 1 -e 32  --measure_latency --fuse_conv_bn
  • -e controls the batch size of input on GPU while the batch size of input is fixed internally to 1 on CPU/ARM.

5. Training

FasterNet-T0 training on ImageNet-1K with a 8-GPU node:

python train_test.py -g 0,1,2,3,4,5,6,7 --num_nodes 1 -n 4 -b 4096 -e 2000 \
--data_dir ../../data/imagenet --pin_memory --wandb_project_name fasternet \
--model_ckpt_dir ./model_ckpt/$(date +'%Y%m%d_%H%M%S') --cfg cfg/fasternet_t0.yaml

To train other FasterNet variants, --cfg need to be changed. You may also want to change the training batch size -b.

Acknowledgement

This repository is built using the timm , poolformer, ConvNeXt and mmdetection repositories.

Citation

If you find this repository helpful, please consider citing:

@article{chen2023run,
  title={Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks},
  author={Chen, Jierun and Kao, Shiu-hong and He, Hao and Zhuo, Weipeng and Wen, Song and Lee, Chul-Ho and Chan, S-H Gary},
  journal={arXiv preprint arXiv:2303.03667},
  year={2023}
}