Isomorphic Pruning

Official implementation of the ECCV'24 paper Isomorphic Pruning for Vision Models.

Isomorphic Pruning has been integrated into Torch-Pruning (v1.4.1).

Isomorphic Pruning for Vision Models
Gongfan Fang, Xinyin Ma, Michael Bi Mi, Xinchao Wang
Learning and Vision Lab, National University of Singapore

Intro

Pre-requisites

Packages

pip install -r requirements.txt

ImageNet-1K

Please prepare the ImageNet dataset under ./data/imagenet as follows:

data
├── imagenet
│   ├── train
│   │   ├── n01440764
│   │   ├── n01443537
│   │   ├── n01484850
│   │   ├── n01491361
│   └── val
│   │   ├── n01440764
│   │   ├── n01443537
│   │   ├── n01484850
│   │   ├── n01491361

Quick Start

1. Download our pruned models (Optional)

Model	#MACs	#Params	Top-1 Acc. (%)	Checkpoints
DeiT-4.2G	4.16 G	20.69 M	82.41	Download
DeiT-2.6G	2.61 G	13.07 M	81.13	Download
DeiT-1.2G	1.21 G	5.74 M	77.50	Download
DeiT-0.6G	0.62 G	3.08 M	72.60	Download
ConvNext-S-8.5G	8.48 G	47.36 M	83.17	Download
ConvNext-T-4.2G	4.19 G	25.32 M	82.19	Download

The full list of pruned models can be found here

mkdir pretrained && cd pretrained
wget https://github.com/VainF/Isomorphic-Pruning/releases/download/v0.1/deit_4.2G_isomorphic.pth

pretrained
├── convnext_small_8.5G_isomorphic.pth
├── convnext_tiny_4.2G_isomorphic.pth
├── deit_0.6G_isomorphic.pth
├── deit_1.2G_isomorphic.pth
├── deit_2.6G_isomorphic.pth
└── deit_4.2G_isomorphic.pth

You can evaluate the pruned models using the following command:

python evaluate.py --model pretrained/deit_4.2G_isomorphic.pth --interpolation bicubic

MACs: 4.1626 G, Params: 20.6943 M
Evaluating pretrained/deit_4.2G_isomorphic.pth...
100%|███████████████| 782/782 [01:57<00:00,  6.68it/s]
Accuracy: 0.8241, Loss: 0.8036

2. Evaluate pre-trained models from Timm

Evaluate the performance of the pre-trained models from Timm on ImageNet validation set.

bash scripts/evaluation/deit_small_distilled_patch16_224.fb_in1k.sh

MACs: 4.6391 G, Params: 22.4364 M
Evaluating deit_small_distilled_patch16_224.fb_in1k...
100%|█████████████| 782/782 [02:00<00:00,  6.51it/s]
Accuracy: 0.8117, Loss: 0.7511

3. Pruning

Perform isomorphic pruning on the pre-trained models. We use data-driven method to estimate the importance of parameters. It will accumulate the importance scores over multiple batches.

bash scripts/pruning/deit_4.2G.sh

Summary:
MACs: 17.69 G => 4.17 G
Params: 87.34 M => 20.72 M
Saving the pruned model to output/pruned/deit_4.2G.pth...

4. Finetuning

Finetune the pruned model and save the intermediate/latest/best checkpoints under output/finetuned.

bash scripts/finetuning/deit_4.2G.sh

5. Evaluate the pruned model

The pruned model will be saved as a .pth file with the model definition. We can directly load the .pth to obain the pruned model. You can also pass the timm model name to the script to download the pre-trained model and evaluate it.

# bilinear for ResNet and bicubic for other models
python evaluate.py --model PATH_TO_PRUNED_MODEL --interpolation bicubic

Results for DeiT

Known Issues

The ConvNext models in our paper were finetuned using the official implementation. We provide an isolated script convnext_train.py for ConvNext finetuning and will unify the training scripts in the future.

Acknowledgement

This implementation is based on Timm and Torchvision Reference.

BibTeX

@article{fang2024isomorphic,
  title={Isomorphic Pruning for Vision Models},
  author={Fang, Gongfan and Ma, Xinyin Tan and Mi, Michael Bi and Wang, Xinchao},
  journal={arXiv preprint arXiv:2407.04616},
  year={2024}
}