Visual_attention_tf

A set of image attention layers implemented as custom keras layers that can be imported dirctly into keras

Currently Implemented layers:

• Pixel Attention : Efficient Image Super-Resolution Using Pixel Attention(Hengyuan Zhao et al)
• Channel Attention : CBAM: Convolutional Block Attention Module(Sanghyun Woo et al)

Installation

You can see the projects official pypi page : https://pypi.org/project/visual-attention-tf/

pip install visual-attention-tf

Usage:

fromtensorflow.keras.modelsimportModelfromtensorflow.keras.layersimportInput, Conv2Dfromvisual_attentionimportPixelAttention2D , ChannelAttention2Dinp=Input(shape=(1920,1080,3))
cnn_layer=Conv2D(32,3,,activation='relu', padding='same')(inp)
# Using the .shape[-1] to simplify network modifications. Can directly input number of channels as wellPixel_attention_cnn=PixelAttention2D(cnn_layer.shape[-1])(cnn_layer)
Channel_attention_cnn=ChannelAttention2D(cnn_layer.shape[-1])(cnn_layer)