KAConv: Kernel Attention Convolutions

KAConv: Kernel attention convolutions

Requirement

• pytorch 1.1.0+
• torchvision
• tensorboard 1.14+
• numpy
• pyyaml
• tqdm
• pillow

Dataset

• ImageNet-1K

Introduction

This is a PyTorch implementation of KAConv.

KAConv embeds attention weights into convolution kernels, so that the model can dynamically adjust kernel parameters at different spatial positions of convolution kernel according to different inputs.

Method

The architecture of KAConv.

The architecture of AG-L unit and AG-H unit.

Experiments

Performance on ImageNet-1K of different baselines

Architecture	Top-1 error	Top-5 error	FLOPs	params
ResNet50	23.30	6.79	4.11G	25.56M
KAResNet50-L	22.27	6.24	4.15G	27.83M
KAResNet50-H	21.92	6.08	4.16G	35.56M
ResNet-101	22.28	6.10	7.84G	44.55M
KAResNet101-L	21.80	6.03	7.90G	49.44M
KAResNet101-H	21.28	5.84	7.92G	66.08M
ResNeXt50	22.35	6.43	4.26G	25.03M
KAResNeXt50-L	21.97	6.24	4.34G	31.27M
KAResNeXt50-H	21.23	6.02	4.36G	53.03M
MobileNetV2 .5x	35.62	14.55	138.46M	1.97M
KAMobileNetV2-L .5x	33.72	13.14	180.52M	4.15M
KAMobileNetV2-H .5x	33.09	12.77	189.00M	11.69M
MobileNetV2 1x	28.09	9.66	315.41M	3.5M
KAMobileNetV2-L 1x	26.83	8.74	378.71M	9.31M
KAMobileNetV2-H 1x	27.01	9.08	401.46M	29.52M

Usage

• Uncomment the runid attribute in the config file
• download the pretrained weight (KAResNet50-L) from here.
• place the weight in runs/ka/10086/best_model.pkl.
• run test,py

Todo

• Modify the source code of the convolutional layer