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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