semvec/pypi-packages huggingface dataset:
visual-attention-tf 1.2.0
CNN Attention layer to be used with tf or tf.keras
License: MIT
Requires Python: >=3.6

# Visual_attention_tf
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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)](https://arxiv.org/abs/2010.01073)
* Channel Attention : [CBAM: Convolutional Block Attention Module(Sanghyun Woo et al)](https://arxiv.org/abs/1807.06521)
* Efficient Channel Attention : [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://arxiv.org/abs/1910.03151)

## Installation
You can see the projects official pypi page : https://pypi.org/project/visual-attention-tf/
```bash
pip install visual-attention-tf
```
> Use --no-dependencies if you have tensorflow-gpu installed already
# Usage:

```python
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D
from visual_attention import PixelAttention2D , ChannelAttention2D,EfficientChannelAttention2D

inp = 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 well
Pixel_attention_cnn = PixelAttention2D(cnn_layer.shape[-1])(cnn_layer)
Channel_attention_cnn = ChannelAttention2D(cnn_layer.shape[-1])(cnn_layer)
EfficientChannelAttention_cnn = EfficientChannelAttention2D(cnn_layer.shape[-1])(cnn_layer)
```