CNN_Attention_Module

Create Model

from Model_ResNet import *

model = resnet18(num_classes=nc, Attention='SE', AttPos='STD')  

Available Model

resnet18
resnet34
resnet50
resnet101
resnet152
resnext50_32x4d
resnext101_32x8d
wide_resnet50_2
wide_resnet101_2

Available attention module

'SE' : Squeeze-and-Excitation Module
'GC' : Global Context Module
'CBAM' : Convolutional Block Attention Module
'BAM' : Bottleneck Attention Module
'TA' : Triplet Attention Module

Available module position

'STD'
'PRE'
'POST'
'ID'

Reference:

1. Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu. Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Dec 2018.
2. Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu. GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2019.
3. Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon. CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Jul 2018.
4. Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon. BAM: Bottleneck Attention Module. BMVC. Jul 2018.
5. Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, Qibin Hou. Rotate to Attend: Convolutional Triplet Attention Module. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Nov 2021.