• Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks [CVPR 2023]
• • Image Classification
  • • 1. Dependency Setup
    • 2. Dataset Preparation
    • 3. Pre-trained Models
    • 4. Evaluation
    • 5. Training
  • Acknowledgement
  • Citation

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}
}