基于tensorflow2.5,使用随机池化和L2池化替换CBAM注意力机制模块里的池化操作，并给出使用示例

CBAM注意力机制模块中的池化操作包括全局最大池化和全局平均池化。我们可以使用随机池化和L2池化来替换这些操作。

随机池化是一种随机抽样的池化方法，可以在一定程度上增加模型的鲁棒性和泛化能力。L2池化可以有效地压缩信息，减少模型的计算复杂度和参数数量。

下面是使用随机池化和L2池化替换CBAM注意力机制模块里的池化操作的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Multiply, Add, Activation, Lambda
from tensorflow.keras import backend as K

class CBAM(tf.keras.Model):
    def __init__(self, reduction_ratio=0.5):
        super().__init__()
        self.reduction_ratio = reduction_ratio

    def build(self, input_shape):
        self.channel_axis = -1
        self.max_pool = tf.keras.layers.Lambda(lambda x: tf.reduce_max(x, axis=[1,2], keepdims=True))
        self.avg_pool = tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=[1,2], keepdims=True))
        self.shared_layer_one = tf.keras.layers.Dense(units=int(input_shape[self.channel_axis])//self.reduction_ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
        self.shared_layer_two = tf.keras.layers.Dense(units=int(input_shape[self.channel_axis]), kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
        self.sigmoid_gamma = tf.keras.layers.Activation('sigmoid')

    def call(self, inputs):
        max_pool = self.max_pool(inputs)
        avg_pool = self.avg_pool(inputs)

        gamma = self.shared_layer_two(self.shared_layer_one(tf.concat([max_pool, avg_pool], axis=self.channel_axis)))
        gamma = self.sigmoid_gamma(gamma)

        return inputs * gamma

使用随机池化和L2池化替换CBAM注意力机制模块中的池化操作的代码如下：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Multiply, Add, Activation, Lambda
from tensorflow.keras import backend as K

class CBAM(tf.keras.Model):
    def __init__(self, reduction_ratio=0.5):
        super().__init__()
        self.reduction_ratio = reduction_ratio

    def build(self, input_shape):
        self.channel_axis = -1
        self.random_pool = tf.keras.layers.Lambda(lambda x: tf.math.reduce_mean(tf.random.shuffle(x), axis=[1,2], keepdims=True))
        self.l2_pool = tf.keras.layers.Lambda(lambda x: tf.math.sqrt(tf.math.reduce_mean(tf.math.square(x), axis=[1,2], keepdims=True)))
        self.shared_layer_one = tf.keras.layers.Dense(units=int(input_shape[self.channel_axis])//self.reduction_ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
        self.shared_layer_two = tf.keras.layers.Dense(units=int(input_shape[self.channel_axis]), kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
        self.sigmoid_gamma = tf.keras.layers.Activation('sigmoid')

    def call(self, inputs):
        random_pool = self.random_pool(inputs)
        l2_pool = self.l2_pool(inputs)

        gamma = self.shared_layer_two(self.shared_layer_one(tf.concat([random_pool, l2_pool], axis=self.channel_axis)))
        gamma = self.sigmoid_gamma(gamma)

        return inputs * gamma

使用示例：

import tensorflow as tf
from cbam import CBAM

inputs = tf.keras.layers.Input(shape=(224,224,3))
x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(inputs)
x = CBAM()(x)
x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(x)
x = CBAM()(x)
x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu')(x)
x = CBAM()(x)
x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu')(x)
x = CBAM()(x)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
outputs = tf.keras.layers.Dense(units=10, activation='softmax')(x)

model = tf.keras.Model(inputs, outputs)
model.summary()