fhfeishi / parc-net

[ECCV 2022] Source code of "EdgeFormer: Improving Light-weight ConvNets by Learning from Vision Transformers"

ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer

中文版

ParC-Net ECCV 2022

This reposity was named EdgeFormer, which is changed to ParC-Net, as "Former" indicates that the model is some variant of transformer.

Official PyTorch implementation of ParC-Net

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Introduction

Recently, vision transformers started to show impressive results which outperform large convolution based models significantly. However, in the area of small models for mobile or resource constrained devices, ConvNet still has its own advantages in both performance and model complexity. We propose ParC-Net, a pure ConvNet based backbone model that further strengthens these advantages by fusing the merits of vision transformers into ConvNets. Specifically, we propose position aware circular convolution (ParC), a light-weight convolution op which boasts a global receptive field while producing location sensitive features as in local convolutions. We combine the ParCs and squeeze-exictation ops to form a meta-former like model block, which further has the attention mechanism like transformers. The aforementioned block can be used in plug-and-play manner to replace relevant blocks in ConvNets or transformers. Experiment results show that the proposed ParC-Net achieves better performance than popular light-weight ConvNets and vision transformer based models in common vision tasks and datasets, while having fewer parameters and faster inference speed. For classification on ImageNet-1k, ParC-Net achieves 78.6% top-1 accuracy with about 5.0 million parameters, saving 11% parameters and 13% computational cost but gaining 0.2% higher accuracy and 23% faster inference speed (on ARM based Rockchip RK3288) compared with MobileViT, and uses only 0.5× parameters but gaining 2.7% accuracy compared with DeIT. On MS-COCO object detection and PASCAL VOC segmentation tasks, ParC-Net also shows better performance.

ParC block

Position aware circular convolution

Experimental results

EdgeFormer-S

Tasks	performance	#params	pretrained models
Classification	78.6 (Top1 acc)	5.0	model
Detection	28.8 (mAP)	5.2	model
Segmentation	79.7 (mIOU)	5.8	model

Inference speed

We deploy the proposed EdgeFormer and baseline on widely used low power chip Rockchip RK3288 and DP chip for comparison. DP is the code name of a in house unpublished low power neural network processor that highly optimizes the convolutions. We use ONNX [1] and MNN to port these models to RK3288 and DP chip and time each model for 100 iterations to measure the average inference speed.

Models	#params (M)	Madds (M)	RK3288 inference speed (ms)	DP (ms)	Top1 acc
MobileViT-S	5.6	2010	457	368	78.4
ParC-Net-S	5.0 (-11%)	1740 (-13%)	353 (+23%)	98 (3.77x)	78.6 (+0.2%)

Applying Edgeformer designs on various lightweight backbones

Classification experiments. CPU used here is Xeon E5-2680 v4. *Authors of EdgeViT do not clarify the type of CPU used in their paper. ** We train ResNet50 with training strategy proposed in ConvNext. ResNet50 achieves 79.1 top 1 accuracy, which is much higher than 76.5 the accuracy reported in the original paper.

Models	# params	Madds	Devices	Speed(ms)	Top1 acc	Source
MobileViT-S	5.6 M	2.0G	RK3288	457	78.4	ICLR 22
ParC-Net-S	5.0 M	1.7G	RK3288	353	78.6	Ours
MobileViT-S	5.6 M	2.0G	DP	368	78.4	ICLR 22
ParC-Net-S	5.0 M	1.7G	DP	98	78.6	Ours
ResNet50	26 M	2.1G	CPU	98	79.1**	CVPR 22 new training setting
ParC-ResNet50	24 M	2.0G	CPU	98	79.6	Ours
MobileNetV2	3.5 M	0.3G	CPU	24	70.2	CVPR 18
ParC-MobileNetV2	3.5 M	0.3G	CPU	27	71.1	Ours
ConvNext-XT	7.4 M	0.6G	CPU	47	77.5	CVPR 22
ParC-ConvNext-XT	7.4 M	0.6G	CPU	48	78.3	Ours
EdgeViT-XS	6.7 M	1.1G	CPU*	54*	77.5	Arxiv 22/05

Detection experiments

Models	# params	AP box	AP50 box	AP75 box	AP mask	AP50 mask	AP75 mask
ConvNext-XT	-	47.2	65.6	51.4	41.0	63.0	44.2
ParC-ConvNext-XT	-	47.7	66.2	52.0	41.5	63.6	44.6
ResNet-50	-	47.5			41.1
ParC-ResNet-50	-	48.1			41.8

Segmentation experiments

Models	# params	mIoU	mACC	aACC
ConvNext-XT	-	42.17	54.18	79.72
ParC-ConvNext-XT	-	42.32	54.48	80.30
ResNet-50	-	42.27		79.88
ParC-ResNet-50	-	43.85		80.43
MobileNetv2	-	32.80		74.42
ParC-MobileNetv2	-	35.13		75.73

ConvNext block and ConvNext-GCC block

In terms of designing a pure ConvNet via learning from ViTs, our proposed ParC-Net is most closely related to a parallel work ConvNext. By comparing ParC-Net with Convnext, we notice that their improvements are different and complementary. To verify this point, we build a combination network, where ParC blocks are used to replace several ConvNext blocks in the end of last two stages. Experiment results show that the replacement signifcantly improves classification accuracy, while slightly decreases the number of parameters. Results on ResNet50, MobileNetV2 and ConvNext-T shows that models which focus on optimizing FLOPs-accuracy trade-offs can also benefit from our ParC-Net designs. Corresponding code will be released soon.

Installation

We implement the ParC-Net with PyTorch-1.9.0, CUDA=11.1.

PiP

The environment can be build in the local python environment using the below command:

pip install -r requirements.txt

Dokcer

A docker image containing the environment will be provided soon.

Training

Training settings are listed in yaml files (./config/classification/xxx/xxxx.yaml, ./config/detection/xxx/xxxx.yaml, ./config/segmentation/xxx/xxxx.yaml )

Classifiction

cd EdgeFormer-main
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main_train.py --common.config-file ./config/classification/edgeformer/edgeformer_s.yaml

Detection

cd EdgeFormer-main
CUDA_VISIBLE_DEVICES=0,1,2,3 python main_train.py --common.config-file --common.config-file config/detection/ssd_edgeformer_s.yaml

Segmentation

cd EdgeFormer-main
CUDA_VISIBLE_DEVICES=0,1,2,3 python main_train.py --common.config-file --common.config-file config/segmentation/deeplabv3_edgeformer_s.yaml

Evaluation

Classifiction

cd EdgeFormer-main
CUDA_VISIBLE_DEVICES=0 python eval_cls.py --common.config-file ./config/classification/edgeformer/edgeformer_s.yaml --model.classification.pretrained ./pretrained_models/classification/checkpoint_ema_avg.pt

Detection

cd EdgeFormer-main
CUDA_VISIBLE_DEVICES=0 python eval_det.py --common.config-file ./config/detection/edgeformer/ssd_edgeformer_s.yaml --model.detection.pretrained ./pretrained_models/detection/checkpoint_ema_avg.pt --evaluation.detection.mode validation_set --evaluation.detection.resize-input-images

Segmentation

cd EdgeFormer-main
CUDA_VISIBLE_DEVICES=0 python eval_seg.py --common.config-file ./config/detection/edgeformer/deeplabv3_edgeformer_s.yaml --model.segmentation.pretrained ./pretrained_models/segmentation/checkpoint_ema_avg.pt --evaluation.segmentation.mode validation_set --evaluation.segmentation.resize-input-images

Acknowledgement

We thank authors of MobileVit for sharing their code. We implement our EdgeFormer based on their source code. If you find this code is helpful in your research, please consider citing our paper and MobileVit

@inproceedings{zhang2022parcnet,
  title={ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer},
  author={Zhang, Haokui and Hu, Wenze and Wang, Xiaoyu},
  booktitle={European Conference on Computer Vision},
  pages={},
  year={2022}
}

@inproceedings{mehta2021mobilevit,
  title={Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer},
  author={Mehta, Sachin and Rastegari, Mohammad},
  journal={ICLR},
  year={2022}
}