SEnet --se module

最新推荐文章于 2024-01-23 19:47:55 发布

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最新推荐文章于 2024-01-23 19:47:55 发布 · 1.5k 阅读

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这是SEnet 的特征融合部分，

import tensorflow as tf
from tflearn.layers.conv import global_avg_pool

class SE_layer():
    def __init__(self, x, training=True):
        self.training = training

    def Global_Average_Pooling(self, x):
        return global_avg_pool(x, name='Global_avg_pooling')

    def Fully_connected(self, x, units=3, layer_name='fully_connected') :
        with tf.name_scope(layer_name) :
            return tf.layers.dense(inputs=x, use_bias=False, units=units)

    def Relu(self, x):
        return tf.nn.relu(x)
    def Sigmoid(self, x) :
        return tf.nn.sigmoid(x)

    def squeeze_excitation_layer(self, input_x, ratio, layer_name):
        with tf.name_scope(layer_name) :
            squeeze = self.Global_Average_Pooling(input_x)

            excitation = self.Fully_connected(squeeze, units=int(input_x.shape[3])/ratio, layer_name=layer_name+'_fully_connected1')
            excitation = self.Relu(excitation)
            excitati